Above is a refined version of the mapping exercise that I undertook with my group of 4 in our tutorial last week. This process of developing a problem statement and brainstorming lead to a variety of possible design propositions explored in my most recent post. Initially my problem statement was too broad and wouldn’t have allowed for in depth insights and refined solutions, and so for the first half of the exercise we were brainstorming different elements of the solutions, for example the language, angle, tone, ideal outcome and how to elaborate upon previous research that had been undertaken.
About half way through the process we began developing possible solutions to the problem statement and on the map these can be seen 2-3 components away from the centre. I was really interested in the idea of analysing language and tone as I’d recently undertaken the data scraping task for blog 6 and was fascinated by the way that single-sex rights and liberation groups on Reddit spoke about the other gender, with an ingrained sense of contempt and casual use of derogatory language that appeared to be permissible amongst these communities. A barrier of the brainstorming here was that no one in our group had experience with or understanding of how to quantify tone or whether there were any bots or algorithms that have the capability to register tone and language to this extent. I think I will continue down this kind of path for my final proposal, whether it is more generative or visualisation-driven, as we were able to come up with some really interesting options to take further.
A benefit to undertaking this task extended beyond just my own brainstorming period; helping others develop their proposals gave me a glimpse into different areas of the board topic of gender equality that I hadn’t investigated myself. For some reason I felt that my own concept development was stronger when working with the other topic areas, so whether I’m currently blinkered in my own approach or I’ve exhausted all solutions in a narrow area I’m not sure, but it was a little disheartening to not feel as confident with my own brainstorm. In hindsight, I feel that this process would have been much more effective if I had been able to completely clear my mental slate and approach the topic without ideas already in my head. This will be something to consider for next brainstorm I think!
Up until this point I had tried to be immersed and completely focused on the topic of gender equality and feminism as much as possible without really considering options for a design proposal as a response to the issue. Undertaking this brainstorming task with peers who were equally immersed in the same issue made this a lot more interesting and beneficial to my idea development. In the development of the issue statement to direct the brainstorming, I didn’t have a very succinct or specific explanation of my issue, and as a result of being quite broad, at the time my insights and possible solutions were quite bland.
Developing the problem statement by following the Who, What, When, Where, Why method was a really good way to better understand the issue and lead to some potential outcomes.Repeating and elaborating on this process individually later though, I began to brainstorm some potential directions for tackling the issue in a more tapered and structured way around the topic of how online communities engage with feminism across social media.
Who Does the Problem Affect?
The issue of Gender Equality affects everyone, however not all in the same way or with the same level of pervasiveness. This can be specified further by considering those frequently engaging with social media and online communities, such as Reddit and Facebook. This audience consists predominantly of late teens to late thirty year olds as a rough estimate. Further still, whilst both sexes are affected by gender inequality, there are sub groups that equally affected but, again, have varying levels of action and engagement. These include men’s rights, anti-feminist movements, pro-feminist groups, LGBTIQ advocates, spiritual adherents, and simply the cultures of behaviour that pervade the different online platforms.
What Are the Boundaries of the Problem?
In the simplest of terms, gender inequality affects both men and women, and whilst traditionally this has been an issue tackled by the feminist movement, we are increasingly assessing the impact of excluding men from this discussion, the perpetuation of many double standards, social expectations and stereotypes that are outdated and sexist, and, attributing Feminism to a single sex.
When Does the Problem Occur? When Does it Need to be Fixed?
Gender inequality has been a element of our history from the very beginning. Actually considering the male role in the feminist movement has only arisen over the last few decades, and actions have been taken in an even shorter time frame. As social media has only existed as a key channel of communication for the last 15 years or so, it is only recently that groups have banded together online to share their views on the topic. Due to the nature of social media and the internet, the information and discussion around the problem has all increased tenfold by being able to interact with someone sharing your perspective who lives on the other side of the world. Communities are strengthened in numbers and accessibility and issues arise when opposing views are not able to respectfully debate the issue and work towards mutually agreeable solutions.
As for a deadline for action, there is not an overnight solution. Like racism and homophobia, it has taken generations before a mentality of respect is deeply and intrinsically ingrained in our society enough to speak out against hate. Ideally this is fixed sooner rather than later so we can begin embracing what different sexes have to offer without elitism or sexism.
Where is the problem occurring?
Specifically for social media, the problem is occurring amongst online communities with very subject mentalities towards the issue, and as the problem occurs across a spectrum that includes the impacts on men and women and the oppositions to both stances, each community’s culture of discourse and action makes collaboration and discussion difficult despite the extremely accessible platform for communication. Although for the purposes of this task I was focusing on online communities, the implications of the actions and worldviews formed by actively participating in these groups shapes wider aspects of our society, such as workplace interactions, legislation, social norms and taboos, and cross-cultural collaboration and discussion.
Why is it important that the problem is fixed? What impact does it have on all stakeholders?
In this case I would disagree with the term “fixed”. The status of our current society is a clear outcome ofdeveloping “successfully” as a result of a patriarchal background. We are at a point now of reflection upon the impact of this history and considering how we need to change in order to function successfully in a future civilisation where no one is discriminated based on their sex and people are free to make personal decisions that are not shaped by expectations of their gender. I would say that the term “evolve” is more appropriate, as each generation is being equipped with a mentality to better adapt to the necessity for respect towards both gender that is becoming increasingly prevalent in society today.
5 Possible Outcomes
From the brainstorming process, these are three potential outcomes to address the problem statement.
1. Comparing Language of Women’s and Men’s Rights cultures This would be better suited to subreddits with established extremist communities with their own opinions towards the other sex. I find these pro-single sex groups really interest and my proposal would be a generative visualisation map of the tone and language used across these different subreddits. For example, men’s rights groups, even when speaking matter-of-factly about women have a culture of speaking in very derogatory language about them within their posts and comments. It is really interesting to juxtapose these discourses and approaches to emphasise the lack of cohesiveness and promote action and discussion.
2. Map engagement levels across groups on the genre equality spectrum Similarly to the previous proposal, this would be a more data based visualisation based on generative data. I propose a spectrum of gender equality with the single-sex extremists wings at either and and pure gender equality in the centre, similar to a political spectrum. Along this spectrum would be positioned various groups/pages/subreddits (dependent upon the social media platform) as columns of engagement, based on their stance towards equality. As people subscribe to these different groups or the topics ‘trend’, the columns would be affected, such as becoming higher or brighter to visualise where our weight on the issue, as a society, is actually sitting.
3. Juxtaposing messages of sexism or gender inequality The goal of this proposal is to represent how ingrained in our society gender inequality is. This would work by matching two tweets, for example, with the same phrase relating to gender inequality, such as “I hate it that women…”, or “Why can’t boys…”. By comparing two separate statements it will ideally create small microcosm of the huge spectrum of areas that this issue encapsulates. Further, it would be really interesting to compare statements that are directly related to genders, to highlight the negative phrasing and language that is used against men and women on the internet.
4. Connect people from across the globe with similar perspectives and online interactions Using the benefits of easy communication, I propose utilising a bot to track user location and posts by analysing key phrases and subscribed groups and using this data, connect the two people via either an existing or a new platform. This would be a really interesting way for individuals to gain a more informed understanding of the issue from a different cultural perspective.
5. Twitter-bot reply to anti-equality tweets Using a method of data-scraping and automated posting (e.g. bots), sexist tweets that degrade either males or females would be automatically replied to with a message or link that calls our the sexism. Whilst this would definitely be met with a lot of confrontation I think it would be a really interesting way to help people realise that certain things that are said are in fact sexist or promoting gender inequality.
Hybrid Generative System and Data Visualisation: Juxtaposing Gender-specific Tweets
As contemporary society strives to achieve access to universal gender equality across all areas of life, it must be remembered that both males and females are affected by gender discrimination and movements towards fair outcomes. Gender equality ensures respect, acknowledgement and celebration of individuals and groups without prejudice or criticism.
Achieving equality doesn’t mean simply elevating rights of the oppressed to those of the oppressors, but to provide means for both genders to flourish regardless of sex in an equity-driven culture. Currently, movements towards gender equality are mostly focused upon females having the same rights that males currently uphold, and less focused upon identifying where men’s rights should rise to meet women’s. Due to a history of women’s oppression, as a modern society we are much more accepting of harsh public critique of men, an impact of relatively second-wave feminist propaganda, specifically present in online platforms. Contrastingly, criticism of women is viewed as discrimination and sexism, resulting in resentment and the exclusion of men in the equality discussion. This institutionalised and publicised perpetuation of double standards has lead to feminists gaining a negative stigma and reputation for being hypocritical and male-hating, and men feeling that they can not be open about feeling repressed the way that women are praised for.
The purpose of my proposal is to promote public awareness and reflection of the language and attitudes we frequently employ when discussing the other sex. The final design is a hybrid of a generative system and data visualisation, utilising a Twitter-bot to find, compare and display tweets on a screen-based platform. The process for this bot would be to cycle through a series of phrases directed at both females and males separately and compare them side-by-side, which would continuously update every 5-10 seconds to show a new phrase and tweet pair. By visually juxtaposing tweets that use the same phrasing relating to females and males respectively, the aim is to visualise the spectrum of attitudes and opinions that are expressed on this topic. I anticipate that the most evident display in this system would be the ingrained condemnation and hypo-criticism for one or both sexes, which continues to discourage mutually respectful outcomes.
An example of how this would work is shown in the mock up below (source A). In this case the algorithm has searched for the phrase “I love that women/men…” and have displayed two of the corresponding tweets in juxtaposition. As is evident in this example, the attitude and tone in each tweet are completely different, with the first applauding women for creating empowerment from their over-sexualisation, and the second sarcastically calling out men for sexual assault crimes reflected in a patriarchal judicial system. In this one example we can see how the public opinion on this topic is very disparaging of men whilst simultaneously praising the same actions performed by women.
In a second example (source B), using the same process but with the phrase “I love that girls/guys…”, a completely different attitude towards specific genders is represented. It is interesting that these two examples praise actions that subvert traditional gender roles and thus provide an insight into how we are really embracing acts towards mutually beneficial gender equality. Further, by cycling through different words to describe males and females, a greater scope in opinions can be reached, as more colloquial tones tend to be used for praising, whilst formal vernacular is often linked with criticism. In this case referring to females and males as girls and guys creates a much more light-hearted tone and yields vastly different results to the previous example.
As the twitter-bot would not be able to consistently and accurately identify the tone or angles used in either tweet in the pairing, this would reveal some really interesting comparisons. The table (source C) below highlights the combinations of tweets opinions and the result of the juxtaposition.
The idyllic end goal is that as both individuals and wider society we become more aware of how we speak about the opposite sex, particularly on social media where as many as 50% of the users could be offended by a sexist generalisation that is the result of an ignorant interaction with a minority. This is the first step in extending the hand of respect that will take us one step further to embracing gender equality.
Twitter is a social networking media, which allows users to publish and share messages that are visible to other users. These messages should be limited in the 140 characters or less in twitter, uses can found lots of different users on twitter, which is include basic communication between friends and family, a way to publicise an thing, or companies use this tool to communicate with their clients. Twitter was founded in 2006, and was the third most popular social network media after Facebook and Myspace.
Data Pipeline is an embedded data processing engine for the Jave Virtual Machine. Users can use it to convert incoming data to a common format, prepare data, migrate between databases. replace batch jobs with real-time data. Data Pipeline is very easy and simple, uses can quickly learn and use.
I was typed my issue( refugees and asylum seekers) in Data Pipeline search, firstly, I received 500 tweets, then if need more data, you can chose search more than 500 tweets, also you can download tweets to excel, and you can choose emailed results to you daily.
This week, in order to extend my knowledge of my chosen issue, being the factors that influence a person’s stance on climate change, I undertook a data scraping exercise using Twitter. Twitter is a social media platform which allows uses with an account to ‘tweet’ their own messages as well as ‘retweet’ the messages of others. The retweeting feature creates a kind of network amongst users, and, in a way, allows users to express their point of view without being directly connected to the tweet as they did not write it. Twitter is limiting in the amount of detail that users can go into in their tweets because of the 140 character limit that applies. It is a very sharing-oriented platform, as are most social media platforms, and the use of hashtags allows tweets to be grouped together, which can generate interesting and often unexpected connections between tweets. In undertaking some broad research into the use of Twitter in the climate change debate, I came across an article by Simon Pollock which said that “most social studies show online interaction is reinforcing pre-existing beliefs and values, rather than opening minds”. This was very interesting to me, as it highlighted that the nature of social media is such that it often groups people of similar views together, as opposed to generating discussion amongst groups with opposing views.
I used the Advanced Search feature on Twitter in order to conduct my data scrape. Whilst creating a Twitter bot would potentially allow access to a deeper, more refined, and more specific data set, I am limited in my coding ability, so this is beyond my capabilities at the moment. However, I would like to explore in the future the possibilities opened up and patterns that can emerge in tweets through the creation of a Twitter bot.
Data Scrape 1
My initial data scrape involved the very general search term of ‘global warming’, with no other parameters. As could be expected, this provided an extremely broad spectrum of tweets from people across the world, reminding me of the international reach both of the issue of global warming, and of Twitter as a social network. The first feature that caught my attention on this initial data scrape was the fact that a tweet by Barack Obama, the American President, was the top tweet in the global warming category by virtue of the number of likes it had received so far.
This interested me greatly because it shows the amount of attention a tweet by a well-known figure can receive and, by extension, how much influence this figure can have on the general population. It suggests that if someone is popular or well-known, their point of view on controversial topics such as climate change, is likely to be viewed many times and thus influence the views of their followers. In relation to this, it could be seen that on the platform of Twitter, the views of well-known people are the views that will be spread around and talked about, whilst the views of ordinary people, even though they are important, will be lost amongst hundreds of other tweets. In this way, Twitter as a social media platform has incredible power to inform people about what other people’s opinions are on controversial topics, and to subsequently influence their opinions.
Within this general search, as I was scrolling through the tweets, I came to notice that there were several relating to various levels of concern about the lack of action being taken by governments, who are major stakeholders or ‘actors’ in the issue of climate change. In this, I also found that there were several people who were suggesting that climate change is a hoax being perpetuated by governments. This was extremely interesting, and a view I have not come across before.
This raised the idea that governments across the world need to do more to tackle climate change, an opinion which has been recurrent in my other research, particularly in my visual research, as well as the idea that governments perhaps need to communicate more with the general population as to their reasoning behind their lack of action. In this general search, I also came to notice the #blacklivesmatter hashtag cropping up repeatedly, in response to a protest in the UK claiming that global warming is racist.
This raised for me an important thought around the idea of trends on Twitter, and the potential issues this may create in regards to the legitimacy of the opinions shared by people on Twitter—they may simply tweet about a particular ‘trending’ issue to make themselves appear informed, and in order to suggest to their followers that they are actively invested in these issues, particularly when they are related to social justice causes. Another issue raised around ‘trending’ topics on Twitter is the potential for incorrect and uninformed viewpoints on different issues to be widely circulated, causing people to become confused, as well as perhaps to form incorrect views about the issues.
I found that across a lot of the tweets about global warming there were links to news articles, demonstrating that these are the sources that people get a lot of their information about climate change from, rather than from more trustworthy journal articles and other scholarly sources. This finding also really made me think about how far these not-so-trustworthy sources can travel and how easily accessible they are to people. I would suggest that these sources are a major factor in influencing people’s views on climate change because they are sources which generally get shared on social media platforms, and because they are convincing in their writing style, which often contains bias. I found that the majority of tweets were quite biased and opinionated themselves in their tone, perhaps a result of the strict character limits on the tweets, which force people to get their point across in a very limited amount of space. However, whilst this emotive quality of tweets gives an interesting and first-hand account of the feelings people have towards climate change, it also warrants caution as it shows that people are only pushing their view of the argument, presumably with limited consideration of other points of view.
Data Scrape 2
Whilst this initial search provided me with some excellent general insights into both the discussion around global warming, and features of Twitter itself, I found that there was no specific information that I could gather which would be useful for my focus area of factors that influence a person’s views on climate change. As such, inspired by the ‘World of Change’ data visualisation project I examined in blog post 4, I decided to search for tweets within a particular city. Still using the search term ‘global warming’, I conducted 3 separate searches, one for Sydney, one for New York, and one for New Delhi. In undertaking these searches, I noticed that whilst the tweets for Sydney and New Delhi were more general and focused on a range of concerns about the causes and effects of global warming across the world, the tweets for New York were focused on the recent floods in Louisiana, with many citing sources that blamed the floods on global warming.
This demonstrates the view of Andrew J. Hoffman, whose article I examined in blog post 2, and who states that “…personal experiences with extreme weather, both direct…and indirect…increase individual belief in climate change” (Hoffman 2015, p. 10). This presents to me an interesting insight into the types of events which may convince people with different cultural views of the realities of climate change, and I believe that this idea of geographical proximity, an area that I mapped in week 3, could become an interesting data set to explore.
Data Scrape 3
After conducting these 2 searches, I decided that I still had not gathered any useful specific information about my focus area, the factors that influence a person’s views on climate change. In considering how I could achieve this on Twitter, I came to realise that there is often a lot of information about people in their personal profile, which can be gathered through their log of tweets, their bio, and their country of origin. As such, I again conducted a general search of ‘global warming’, but, instead of taking an overview of all the tweets, I selected a few and went into that person’s profile. There were a few profiles with limited information that could be gathered, however, there were others that I could take a wealth of information from. In these cases, I was able to begin to piece together what factors were influencing the views that person expressed in their tweet. I have included some of these profiles below.
Danielle Peters identifies her account as one “looking at all the ways we are mitigating and adapting to climate change” (Peters 2016). Already from this bio description, it is clear that the individual is very much concerned with climate change, and one could assume that she believes strongly that climate change is occurring, and probably shares her views with others. Upon further inspection, it can be seen that Danielle follows a lot of other conservation and climate aware profiles on Twitter, suggesting that she is very much invested in this cause, and will be influenced by the views expressed by these people and organisations. These assumptions are made clearer through the log of tweets that is available on Danielle’s profile.
I found that whilst she is trying to show people that climate change is real and that it is a big threat, she puts her point across in a very non-threatening way, mostly posting pieces of information such as news articles which she encourages her followers to go and read. She joined Twitter very recently, in August 2016, so there is not a lot of data that can be collected so far as to her influences, but it could be inferred that she takes a lot of her information from general news sources, and that she is already firmly established in her views, so is unlikely to alter her current position on climate change. Also, it could be suggested that Danielle will most likely only tweet articles which match her current views, and also perhaps only look for information which furthers her views.
John Beard is a “news anchor, writer, skeptic, optimist. [His] goal is to make you think, and on occasion…change your thinking” (Beard 2016). He has been a member of Twitter since 2008, and has built up a log of 29 700 tweets. In his bio, there is no specific mention of climate change, and, indeed, on inspection of his tweets, it is clear that this issue is not his only concern. Whilst he tweets regularly about climate change related stories, he also tweets regularly about political issues, and appears to mostly tweet about current news stories in order to encourage his followers to become informed, and also to put his own views forward.
John’s obvious interest in politics stems from his own position as a news anchor, and, as such, it could be suggested that he has formed strong political views through his job. These views are likely to play into his position on climate change, which appears from his tweets to be on of support. The fact that John mostly posts his own tweets, rather than retweeting the posts of others, suggests that he is very sure of his own position on the issues and thus is unlikely to be persuaded to alter his views. The amount of tweets John has linking to news articles suggests that these are the sources he gets most of his information from, also his own position as a news anchor suggests that these are the sources that he is mostly surrounded by. These sources are likely to be the ones shaping, as well as strengthening his views. John’s statement in his bio that his “…goal is to make you think, and on occasion…change your thinking” (Beard 2016) suggests that he is quite influential amongst his friends and followers, and that he is actively trying to get people to engage in his views, and hopefully mould to them.
Tweets that get a lot of likes or that are ‘trending’ have the potential to become extremely influential due to their widespread reach across the world. As such, a tweet about climate change that reached this status could become one of the factors that influences a person’s views on the issue. By extension, a tweet by a prominent figure with hundreds or millions of followers could achieve a similar result.
A lot of people in their tweets about climate change link to news articles. This suggests that these often-biased sources are the main source of information about this issue for a lot of Twitter users. As such, these news articles are a factor in shaping these people’s views on climate change.
The geographical proximity of a person to events that may be seen as being caused by climate change influences their view as to the severity of the issue. If a person is close to a climate change related event, they will be more likely to believe in climate change than someone who is removed from the event.
The information that can be gathered through a person’s bio, tweet log, and country, can provide an insight into their views on a certain issue, in this case climate change. Looking at people’s personal profiles is one method that I could use to gather my own data set around my focus area of the factors that influence a person’s views on climate change, although it is very time consuming, and may not be accurate as people may not put truthful information in their profile.
Climate change is a global issue, and, as such, platforms such as Twitter which collate data from across the world can be very useful tools. They allow people to connect with people in other countries and see what is happening in terms of issues they are interested in. To me, a platform such as Twitter is invaluable as I am able to collect data about areas such as geographical proximity to climate change events and how they affect a person’s views of the issue. However, it is advisable to be wary when using platforms such as Twitter that not all viewpoints may be represented, as only people who feel very passionate about an issue will generally tweet about it, resulting in a lot of tweets presenting extreme views of the issue.
Beard, J. 2016, ‘How Donald Trump retooled his charity to spend other people’s money-The Washington Post’, Twitter post, 9 September, viewed 9 September 2016, <https://twitter.com/jb111?lang=en>
Beard, J. 2016, ‘Scientists See Push From Climate Change in Louisiana Flooding-The New York Times’, Twitter post, 6 September, viewed 9 September 2016, <https://twitter.com/jb111?lang=en>
Beard, J. 2016, ‘Still doubt global warming? U.S. Endures its Sultriest Summer Nights on Record | Dr. Jeff Masters’ WunderBlog’, Twitter post, 6 September, viewed 9 September 2016, <https://twitter.com/jb111?lang=en>
Beard, J. 2016, ‘Trump wants moreUS military spending, ignoring (or not knowing) it’s bigger than next 10 countries combined including Russia and China.’, Twitter post, 7 September, viewed 9 September 2016, <https://twitter.com/jb111?lang=en>
Obama, B. 2016, ‘Rising sea levels are already flooding homes and roads along America’s coasts. The time to #ActOnClimate is now.’, Twitter post, 7 September, viewed 7 September 2016, <https://twitter.com/BarackObama?lang=en>
Peters, D. 2016, ‘Arctic sea ice video shows it has shrunk this year almost to 2012 levels via @NPR @NASA’, Twitter post, 20 August, viewed 10 September 2016, <https://twitter.com/DaniClimate>
Peters, D. 2016, ‘In the village of Ashton Hayes, England, the act of reducing emissions is a fun community project’, Twitter post, 21 August, viewed 10 September 2016, <https://twitter.com/DaniClimate>
Peters, D. 2016, ‘Proud of the research that is happening to understand #climate change in NYC’, Twitter post, 10 September, viewed 10 September 2016, <https://twitter.com/DaniClimate>
The history and context of language are always changing and developing. As the emergence of technology and the Integration of the Internet changes the way we consume media. Our linguistics and vocabulary also expand. Social Media in its own platform is a major contributor in the ways we communicate visually and audibly. The format and structure of social media influences writing styles as well as content. Twitter is a new form of media that delivers its messages in a 140 character limit. This restriction creates a succinct, creative and empowering conversation that users are easily able to engage and scroll through.
Lexicons are a linguistic resource that we use to understand the vocabulary of a person in association to words of sentimental value (emotions). Whether they’re positive, negative or neutral. I.e. ‘NO!’ and ‘no’ conveys a different tone of voice and with the slight alterations in its composition, It delivers a different message. Twitter is a primary social media platform that deals with languages of informal expressions. Generally a collation of data and colloquial expressions. Such as acronyms, the use of incorrect spelling/ terms and abbreviations. Due to the vast majority of language expressions and variable factors, It is difficult to determine whether the responses are of sentimental value (positive, negative or neutral) therefore the use of emoticons are applied.
Emoticons are a highly recognised attribute to the Internet language. The use of visual expression displays a greater range of sentimental values and is a language technique globally practised. Emoticons are considered to be opinion lexicons and are stable for sentimental classification, unlike literal words.
The default Twitter search allows users to add emoticons to the search to find positive/ negative tweets. The majority of tweets does not contain emoticons which impact the search and statistics by DTA: 25th Australasian Database conference shows that only 9.40% of tweets in 2011 contain at least one emoticon. 7.37% of that is positive and 2.03% negative. (Mohammad, S, A. Wang, H. 2014). Due to these results, It shows a decline and insufficient use of lexicons and emoticon limitations.
Twitter features using # syntax as a mean of collating tweets into categories and as a new form of internet language. Hashtags are also a form of metadata by collecting words of the same topic giving context to the tweet. For example #idontwanttowritethisblogpostanymore groups tweets with similar concepts. Although topics that are not typical are often more difficult to evaluate and contribute to the global expansion of lexicons, providing better performance to searches and collation of material.
Social media is a large and prevalent force within society. There are various social media platforms that people can use to obtain and share information on current issues in society. They are a simple way for people to interact and communicate their opinions and beliefs with each other on certain issues in the world. Social media is extremely powerful as it can be an agent for change and can heighten awareness on particular concerns in society.
For this research, I have used Twitter to gain further insight into the perspectives of mental health in Australia. Twitter enables registered users on various devices to send, read and share short messages limited to 140-characters. It is a free online social networking service that many people use to share their opinions on issues and receive information on particular topics. Tweets can be commented on, liked or retweeted and contain conversation threads, hashtags to connect to general topics, hyperlinks to relevant websites and tags to other users. Twitter is a public service so users can follow/ be followed by anyone and tweets are permanent and searchable.
Data Scraping Process
The process I used to collect data was the Twitter Archiver add-on in Google Spread Sheet. Once I had connected my Twitter account to my spread sheet, I created a rule to find tweets catered towards my issue. It took me a few attempts to achieve a good set of data. My more specific searches didn’t bring up any tweets so I first searched broadly using the hashtag #mentalhealth in Australia and in my second search I specified the words stigma, mental and health. This brought up lots of results from many different stakeholders. From here, I went back and forth between the spread sheet and exploring Twitter manually for tweets. Using the spread sheet and Twitter directly, I found this method of data collection quite beneficial and discovered more information about mental health.
First search rule.
First data spread sheet.
Second data spread sheet.
Outcome of my data scraping
Below are some tweets that stood out to me in my data collection and analysis:
Reading through all these tweets from my data spread sheet made me realise that the view of mental health on Twitter is extremely positive. Having researched mental health continually for the past six weeks, it’s hard to see the positive side of the issue. Negativity and stigma are prevalent forces within the issues of mental health but I was pleasantly surprised to see the positivity and support displayed in these tweets. They mostly speak of increasing awareness of mental health issues, boosting positivity and helping spread the word for particular mental health illnesses and campaigns. This data demonstrates the power that social media has today in increasing awareness for particular issues.
Various stakeholders can also be identified through this data. Stakeholders on Twitter vary from people suffering and/or affected by mental health problems, bloggers about mental health and wellbeing, doctors and health professionals and also organisations such as SANE Australia. A lot of opinionated data can be collected from these individual profiles to gain a greater insight into the issues of mental health and how these stakeholders play a part within the issue.
Through my analysis of my data and further research, I have also identified some main hashtags used in relation to mental health which I have categorised into a mind-map (yes, another mind-map) below. Main hashtags that I discovered included:
Other hashtags that were quite prevalent in my searches include:
Hashtags demonstrate what is trending and provides an overview of particular topic, in this case, mental health. Again, it is interesting and enlightening to see that most of the hashtags used are positive and forward thinking.
After wading through all that data, I have created a five point summary about my experience of data scrapping and my view of Twitter:
Positivity stood out amongst the negativity.
Hashtags are annoying, yet helpful for data purposes.
Social media has a great power to boost awareness of issues.
Opinionated data offers a greater insight into various issues.
Use Twitter wisely; anyone can see it.
Visual Design Responses
It is still hard to say at this stage what design responses I could use to visualise this data as the information I have collected is still quite broad and abundant. A possible visual design response for this data on the issues within mental health could manifest as an interactive installation outlining the stakeholders involved and emotions experiences. I believe emotions and empathy is a key factor in understanding mental health issues. An engaging design like an installation would make the issue real to the audience. I would also like to explore the disconnect experienced when articulating ones mental state and how this can be perceived as attention seeking. Again, I could use emotions and feelings experienced by people to perhaps create a generative design response.
Twitter is a social media platform designed around the central feature of sharing 140 character posts with followers, while also following other users in order to receive their posts in a simple easy to digest feed.This functionality has lead to Twitter becoming of the most active discussion based communities on the web, where users cluster around issues and hashtags while also interacting with organisations and news content.
The important difference between Twitter and other social platforms is the circles and connections around issues rather than social circles or friendship groups. This leads to far more vigorous discussion of issues and more interaction with news content and news organisations.This is also seen in how users select who to follow, with many using their twitter feeds as a personally curated news source contributed to by any number of organisations or individuals they are interested in.
This is also one of the most popular tools for interaction between celebrities and other high profile figures and their fans, and the platform has often gathered negative attention due to attacks on high profile figures in the potentially anonymous environment. (Twitter 2016)
In my twitter web scraping, I set myself the goal to find data relating to parental leave for fathers, or paternity leave in Australia. The first search I was able to retrieve data with was based on the search rule:
> paternity OR leave OR feminism OR fathers OR gender OR equality #genderequality
This generated in 2791 tweets over a two week period. The tweets from this search was not specific to Australia, and only a few of them included the words paternity leave. Out of these tweets only 3 had the word ‘paternity’ in it – and none regarding Australia.
I attempted a few searches before in purpose to collect data much more specific to Australia. I experimented with words like: parental leave, fathers, #genderequality etc. These area specific searches gave me no results, so I decided to continue with global twitter searches, but more channeled towards parental leave:
> fathers parental OR leave OR gender OR equality
This generated in 891 tweets. Again I tried to specify this search to only Australia but this gave no result. There were tweets within the successful search which were from Australia, so I became unsure if I was conducting the area search correctly. I tried a more concise search with the rule:
> parental #genderequality
This generated in only 18 tweets, where one tweet had been retweeted over the last couple of weeks:
I explored the #IAmParent campaign which is an initiative from UN Women with basis in the Empower Women organisation. The campaign is a bit more specific to the current situation and urge for change in the US where there is zero federal financial support for mothers and fathers – which also was fact in Australia before 2011 (Department of Social Services 2016).
Most of the tweets I found that related to my topic had basis in the United States or India, and I realised a large part of them related to fathers day. I did a last attempt to scrape for more relevant data, with a search rule which excluded tweets addressing the debate in India, as well as most tweets relating to fathers day:
> fathers paternity OR parental OR australia -YNoLeave4Papa -India -day
This definitely resulted in a scraping more relating to my issue, and I found a few interesting accounts worth exploring.
The search led me to an organisation under the name Fathers4Equality (2013). A lot of the content created by this user related to laws regarding custody and divorce, and family in the event of separation between parents. It is definitely an organisation worth investigating to see how they position themselves with paternity leave and in what areas they experience difficulties.
It would be very interesting to see a visualisation of this data showing the gender split of people who are active around these topics. I was seeing a larger representation of men than I presumed and due to the nature of the medium perhaps views and opinions are expressed truer on Twitter.
Twitter is a powerful tool for realtime collection of opinions across a number of issues and from a huge variety of perspectives.
Successful scraping and collection of this data relies on having a clear understanding of the key terms of your issue and trial and error relating to queries.
The data scraped showed a wide variety of opinions from across the globe and a surprisingly high number of male voices, which tends to be different in the main stream media debate.
Twitter users tended to gravitate to either extreme in their opinions rather than representing a balanced point of view.
Twitter also allows users to present views and positions that may represent a small minority and therefore would not be otherwise seen in the media.
Functioning at its most basic level as an online messaging service, Twitter provides users from all over the world with a platform to communicate, engage with one another and express ideas. Limiting posts to 140 characters or less, the platform encourages quick-fire exchanges of dialogue and conversation about a endlessly wide range of topics, such as news, current affairs, popular culture, humour, beliefs and personal matters.
Trending topic categories, hashtag and re-tweet functions all reinforce the platform’s emphasis on the rapid public dissemination of thought and opinion. A single user’s post has the power to spark a large-scale international debate in a number of minutes, gaining momentum every time the message is re-tweeted, engaged with or replied to by another user.
Encompassing 313 million active users worldwide, it can be argued that the Twitter community is primarily comprised of people who value the right to express their opinion. More than a profile picture or a one-line biography, identity in the Twittersphere is primarily constructed by the opinions, interests and ideas which one chooses to align with. Due to the inevitably polarising nature of this kind of open discussion, expressions of outrage are as common as positive affirmation in the ‘platform where all voices can be heard’ (Twitter 2016).
For an issue as complex and controversial as the Australian refugee and asylum seeker influx, it can thus be argued that Twitter is the perfect platform from which to scrape and analyse direct, passionate public sentiment.
Scraping Attempt 1
For my first scraping exercise, I used Google’s Twitter Archiver to collect tweets containing the hashtag #Refugees or #Asylumseekers or #Auspol.
Unfortunately, the breadth of these topics resulted in an extremely large, and not particularly relevant data set. Collecting almost 25,000 tweets from a period of only one week, a skim through the first few tweets in the data set revealed that many were unrelated to Australia’s treatment of refugees and asylum seekers. For example, the #Auspol hashtag collected sentiments regarding other areas of Australian politics, whilst the #Refugees and #AsylumSeekers hashtags collected discussion on refugee crises in other parts of the world.
Scraping Attempt 2
In order to refine my search to collect data of greater relevance, I decided to conduct a preliminary search on Twitter to gauge which terms and hashtags were likely to return the most interesting information. I created an advanced search for the words “Nauru” or “Manus” or “Detention”, and the hashtags #Nauru or #Manus or #Detention.
Reading through a handful of the tweets collected by this search, I was pleasantly surprised by the increased relevance of the results. By hashtagging the locations of Australia’s detention centres, I had successfully narrowed the topic from all those seeking asylum to only those seeking asylum in Australia. Furthermore, upon reading these tweets I was amazed by the number of users from other countries who were engaging in discussion surrounding Australia’s offshore detention policies.
Scraping Attempt 3
Fascinated by this, I returned to the Twitter Archiver and replicated this search, collecting nearly 9,000 tweets containing #Nauru or #Manus or #Detention from the last ten days. Interested in capturing the sentiment and discourse coming from outside Australia, I looked to the locations specified in each user’s Twitter profile, using conditional formatting to hide all those that contained an Australian location, such as Sydney, QLD, or Gippsland.
Excluding users who listed non-specific or even fictional locations (e.g. Global Citizen or Gaia), I combed through the leftover results, copying and pasting the first 100 tweets into a separate spreadsheet.
Upon reading the contents of these 100 tweets, I found 29 to be irrelevant, containing the hashtag #Detention yet not relating specifically to the Australian issue. From the remaining 71 tweets, I manually compiled a list of user locations, in order to gauge the pervasiveness of the issue from an international context.
The 71 tweets in the sample were posted from a total of 25 different countries. Dominating the sample were England and the United States, with totals of 15 and 11 tweets respectively. After these followed New Zealand and Ireland, with a total 5 tweets each. These results are not surprising due to the active relationships between these countries and Australia, as a result of cultural similarities and the shared English language.
The remaining 21 countries were spread across Europe, Asia, Polynesia and the Middle East, revealing a much greater level of global pervasiveness than I had expected. Interestingly, whilst coming from such a diverse range of locations, the vast majority of these tweets were actually re-tweets of popular statements regarding only a select few issues. These included the recent incident of Danish politicians being denied access to Nauru, the 169 consecutive days of peaceful asylum seeker protests on the island, the leaking of the Nauru files, and a message of support to male detainees on Father’s Day.
Such similarity within this group of tweets does much to highlight the power of the re-tweet function as a disseminator of knowledge within the platform, echoing and spreading ideas at a rapid pace throughout the international Twitter community.
Furthermore, all of these tweets were positioned in objection to Australia’s current systems of offshore detention, echoing the dissatisfaction that is increasingly expressed within Australia, by both the public and the media, regarding the inhumane treatment of refugees on Manus Island and Nauru.
5 Point Summary
Australia’s detainment of refugees is a topic of international discussion within the Twitter platform, evidenced by tweets posted by many users who identify themselves as being outside Australia.
Tweets were sampled from 25 countries in total, spread across Europe, Asia, Polynesia and the Middle East.
Only a small handful of statements were re-tweeted and echoed between this large spread of users and locations.
From the sample taken, England and The United States of America were the two countries most involved in the conversation, attributable to their active relationships with Australia.
The overwhelming consensus from international users was a dissatisfaction with Australia’s treatment of asylum seekers and refugees.
Possible visual responses From these exercises came a number of rich possibilities for a design response. Immediately, I imagined a data visualisation in the form of a world map, in which all tweets about the Australian refugee and asylum seeker issue could be plotted according to the locations of their users. Illuminating both the geographical and proportional spread of discussion surrounding the issue, this visualisation could include interactive functionality, allowing the user to click on a particular country to see the range of opinions expressed there.
Another form of data visualisation could involve the chronological plotting of related tweets along a timeline, categorized by country of origin. Visualisation temporal frequency in the same manner as a heart rate monitor, this design response would communicate the constant spreading and shifting of conversation over time and place.
Lastly, a design response could visualize the geographical trajectory of a single tweet as it is re-tweeted over and over again by users of different origins. Inspired by the small handful of recurring statements present in the data sample I collected, this response would comment on the methods with which information related to the issue is disseminated throughout the global Twitter platform.
Twitter is an interesting program and media. It is a global source that is accessible to anyone that has the internet or a mobile phone, and due to this it redefined the time span for news to be spread or broken. If you want to get a story broken, or spread news about a particular topic, Twitter is your best friend. You aren’t following your particular recipient? No problem. As long as you have an account you can opinion-ate or inform anyone’s eye off–even if it’s not amongst the popular topics of pop culture, technology, breaking news, or politics. Through its hashtags and trending topics, Twitter is easy to navigate, and files everything into neat little boxes–fitted with further hashtags acting as sub-topics.
But what makes Twitter unique? What steps it away from every other social media that keeps people connected and allows sharing? Twitter users are restricted to a 140-character limit in every post. This may sound easy to overcome, but not so much when trying to condense complex readings into a short sentence. Generally used to spread breaking news, natural or human disasters or popular issues, this restriction allows for the point to get across immediately. While keeping it concise means your attention is grabbed instantly, the challenge is shaping the post so that is still makes sense. There is nothing worse than a post with very important words, but nothing connecting them. But the tone of the post also contributes. Most of the posts on Twitter can fall into two categories: opinionated (and biased), or informative (and educated).
With all of this in mind, it was time to undertake the web scraping task. Originally, the Twitter Advanced search paired with the Twitter Archiver Add-on seemed like the ideal program or tool to use. Not only was this task needed, I wanted to use it for my benefit, and expand on my knowledge of the Internet of Things and data privacy in general. The process of scraping the data with the Twitter Advanced search and archiver were simple: the words ‘data’ and ‘ownership’ must be present, and ‘privacy’ was a keyword that could pop up. However, this didn’t turn up much, and it felt that the search was moving away from the original intended issue. A few posts back, the Internet of Things was the focus or specific issue within data that was being investigated. In trying to get back on track, more secondary research was conducted, as well as a repeat of previous class exercises. By doing this, I would hopefully get back onto an issue that was talked about more, and that I could possibly create some visual design responses for.
So here comes the tool Brand24: an online program that business can use to monitor what social media users are saying about their company, with the additional feature of being able to respond to them. With a new focus in mind, a new process was developed–heightened by the added features and functions of Brand24. The first step is for the tool to search the internet for any posts with the exact phrase ‘Internet of Things’, and the added keyword ‘privacy’. From here, the process is to only search through Twitter posts, and then play around with the keywords. Based on the results previously, some key words could be added in to narrow the outcomes further, or another way is to input excluded words to hopefully specify target users or situations. The next stage of this process is to play around with the added features of the influence slider and the emotion scale. The influence slider allows you to see which tweets or people held the most influence in the search in terms of visits, retweets, comments and likes, while the emotion scale allows you to accumulate positive, negative or the default neutral posts. These extra features could aid the process–as well as the type of results–as I could see whether the tool was accurate in its findings, and get to the point straight away on what were the most popular tweets surrounding the issue. The final stages of the process is to visit the top sites tweeted about to expand my understanding of the issue further, and to revisit the saved search often to view the developments.
Below is a flow chart that demonstrates the process that was actually taken in this web scraping task.
The process itself along with the Brand24 tool proved to be a good combination. The detailed and generative process that was designed was enhanced through the features and added functions of the web scraper. The combination allowed me to explore within a topic that was both specific but also broad. I could begin with the broad spectrum such as the Internet of Things, and narrow it down by ‘privacy’ keywords. Also, having excluded keywords such as ‘business’, ‘company’ and ‘patient’ allowed the search to zero in on more generalised posts that were hopefully more targeted to the everyday social media user. It was interesting to see what posts were collated when these aspects weren’t included.
This exclusion did work, however, I felt that the results were very informative and unemotional. Although this was a very common nature with all of the posts gathered. Furthermore, the influence slider was both an advantage and disadvantaged it turned out. It was an advantage because it could narrow down on the most popular tweets in the search, eliminating a lot of the retweets, however it was also a disadvantage, because as the slider was increased, two things happened: mostly all of the results were of about 5 original posts retweeted multiple times, or some of the less retweeted and original content was eliminated–ultimately, a loss.
As implied previously, a lot of the posts were just statements or the name of the article / document attached to the tweet. Or if they were of an opinion, they were direct retweets of the original opinion. This result became difficult as I was hoping to discover some original posts that game an opinion on the privacy issues. However, these were far too rare and possibly due to either the broader spectrum of data and privacy, or the platform of Twitter as its character limit restrictions. Overall, this facet was a little disappointing.
In terms of the Brand24 tool, it seems to make the decision of whether the post is positive, negative, or neutral, however, it often gets it wrong. If there is a negatively associated word in a positive post, then it will only judge the post on that word. Or if there is a link in the post, it just generally puts it as a neutral post. The same outcomes occur if the post is a statement and not an opinion. Therefore, the tool gets it wrong a lot of the times, skewing the results because it possibly lacks the human decision-making element.
With these results in mind, there are a few visual design responses that could arise–however strictly initial concepts. Firstly, a response could be a set of posters or a service design that aims to educate and inform users of the lack of or hidden, privacy in the Internet of Things. Along the same line, the response could be a system or service in the IoT, such as an app that acts as a VPN. It could be a new login screen on social media apps to opt-out of the monitoring. Or another response could be a flyer that is in the boxes of new appliances and products to warn people of its connection to the internet or iCloud.
Since this post was so large in content, ideas and data, here are my findings–of the web scraping and the task altogether.
Twitter allows for short posts but this also restricts what a person can say, conveyed through the extensive retweeting occurring.
With such a broad, new and big topic such as the Internet of Things, most of the posts are informative, and rather statement-based.
It is best to search around for a web scraper or tool that works best for you as it could make the process easier.
Even though the process didn’t work the first time around, I kept trying and changing the parameters until I found something that was both interesting and collated reasonable results. Playing around with the parameters meant that different dynamics could be explored.
When working with data and web scrapers, the task doesn’t always go to plan. Computers don’t think like us humans; they don’t see the emotional side.
Twitter has been defined as a sort of ‘microblogging’ in allowing Its users to post short, 140 character posts on the ‘twitter-sphere’. The limited word count often means that witty and somewhat blunt opinions and statements are made and shared with followers and available to a worldwide community.
The appeal of Twitter is not only the freedom of opinion and expression but the ability to read content quickly and engage with users world-wide. An array of accounts manned by different companies, individuals and businesses means that live events are responded to and can be tracked in real time.
Due to the array of users and audience members there are many different reasons and opinions expressed in ‘tweets’. The different identities and values of the different users are mapped out below:
The tone of voice used in a Tweet very much comes down to the user themselves and The type of user they are. For example, a business company’s tweet about it’s recent successes would most likely have a much more formal and matter-of-fact tone of voice compared to that of a young adult on the topic of a music festival. In saying this companies and even governments/politicians sometimes utilise a more informal and humorous tone of voice over social media platforms to attract a younger and more varied audience.
With over 313 monthly million users and 500 millions tweets per-day, Twitter is a treasure trove of information and opinion. The development of data scraping tools such as the ‘Advanced Search’ Twitter function allows anyone to discover the data and information across the enormous platform. This allows people to examine and explore this data, pulling out interesting findings and insights. I undertook my own data scraping task to investigate the presence of discussions about Type 1 Diabetes, excited see what I could deduce from my process and findings.
My process for data scraping utilised the Twitter Advanced search function as an add-on of Google Sheets. Whilst I began with this special function I ended up using the Advanced Search on the Twitter to enable more thorough data scrapes.
My first search was very broad and included all the related words I had brainstorm in regards to Type 1 Diabetes. This broad search resulted in 170 pages of data and over 10, 000 results. From a brief glance at the results I did immediately recognised that there was a real mix of personal posts from people living with the disease, mixed in with news reports and research findings. I realised however that I needed to refine my data scrape if I was going to be able to properly analyse the data.
My next search involved searching fewer words but still keeping the terms broad by searching ‘Type One Diabetes’, ‘Type 1 Diabetes’ as well as those terms as hashtags. I got 450 results back, a much more realistic set of data however still to big to analyse and draw insight from.
Using the same search terms I wanted to narrow the date field of the data, limiting it to the past 30 days. I discovered that the Twitter Advanced Search add-on for Google sheets didn’t enable a date-based advances search. Instead I ran the advanced search on the Twitter website which did allow me to refine my search by date and time frame. The results weren’t as neatly collated as that of using the search in conjunction with Google Sheets however it did enable me to see entire posts, even those that included images and videos. I was able to spend time looking into the search results and made some insightful discoveries.
I went through the collection of posts and started to tally what type of posts they were. I wasn’t surprised to see that most posts on the topic of Type 1 Diabetes were personal ones that saw people expressing day-to-day struggles of living with the disease. There were certainly fewer news and research based posts than I was expecting and far too many posts about Nick Jonas, a celebrity living with Type 1 Diabetes (I had to give him his own category…). There were of course a few ‘really?!’ tweets where people obviously had no idea about the disease as seen in the example below.
Probably the most incredible discovery I made throughout my data scrape was the discovery of so many support networks and platforms for people living with Type 1 Diabetes to connect with. I was completely blown away and can’t wait to look into these further and discover the digital community that exists.
After I’d completed this area of investigation I was interested in looking at the results I got when searching Type 1 Diabetes imagery on Twitter, again with the Advanced search tool. After creating my own image archive last week I was really intrigued to see what types of images would come up. I found that most images were in regards to personal achievements of those living with Type 1 Diabetes or fundraising or campaigning. I noticed again and as discussed in my recent blog post ‘Type 1 Diabetes: Stakeholders and Visual Representation’ that there was a lack of images that can completely sum up what it is like living with Type 1 Diabetes.
Twitter Advanced Image Search for Type 1 Diabetes 1 (Twitter, 2016)
Twitter Advanced Image Search for Type 1 Diabetes 2 (Twitter, 2016)
Twitter Advanced Image Search for Type 1 Diabetes 3 (Twitter, 2016)
People are using social media to speak out about what It’s like living with Type 1 Diabetes
Social media platforms such as Twitter are acting as a digital support network for people living with diseases as well as other issues and concerns
There are still lots of people who are completely unaware of Type 1 Diabetes or lack any proper understanding of the disease
Most ‘tweets’ about Type 1 Diabetes came from Westernised countires
People living with Type 1 Diabetes are becoming increasingly frustrated with a lack of understanding and stereotypes present in society
In reflection I believe that there may have been more creative potential if I searched some less obvious terms such as ‘insulin pump’ or ‘Autoimmune disease’ etc as part of my data scrape. I also would have liked to have spent more time analysing and looking into the image archives of Twitter however this came as an afterthought to my initial process. I noticed when going back over my search results you can also generate positive or negative responses. It’d be interesting to gather and compare this data, looking at the different tone-of-voice these opposing posts take.
I would also love to have the skill and time to create a Twitter Bot as Chris Gaul talked about in his lecture. It would be fascinating to interact with people posting on this topic and even pose questions to them or provide them with knowledge on Type 1 Diabetes. These bots have so much potential in engaging with the issue through a tweet and therefor engaging with users and people from all walks of life.
Like with all data there is so much potential in how it can be visualised and broken down. A fantastic pre-existing example of this is the #NoPricks campaign image which visualises the average amounts of injections a person living with Type 1 Diabetes has in a month.
With the data and content I’ve collected It would be really great to visualise the breakdown of the types of tweets people post about Type 1 Diabetes as I think it says a lot about social media and sharing and support platform. It’d also be interesting to delve into this further by breaking down the types of personal posts and analysing the reasoning behind why someone may want to post this and the response/support each post gets. This then got me thinking about the possibility of mapping the online support network of sufferers living with Type 1 Diabetes to see the great extent of this network. An example of what this type of data visualisation could look like is seen below:
Muhammad M. U., Muhammad. I , Hassan. S, ‘Understanding Types of Users on Twitter’, Lahore University of Management Sciences, Lahore, Pakistan1 Qatar Computing Research Institute, accessed 1st of September 2016, <email@example.com://www.qcri.org.qa/app/media/4858>
By web scraping the Internet for specific data, it helps with researching and insights others have put up on to a social media platform, for this case, Twitter. By downloading a Google Chrome Extension, Twitter Archiver, when a set of rule is created for this extension it helps search and collate content from Twitter.
I wanted to collect results based on housing affordability in Australia. By creating a set of rules on Twitter Archiver, the results I received was poor, with only 3 tweets. So then I decided to focus more on a topic I was more interested in which was affordable housing. I created a new Twitter search rule, where I included words that can potentially help me with my research and also hashtags that may help refine the search.
Twitter Search Rule: affordable housing, #affordablehousing, #housing and lang: English.
Twitter Archiver accumulated 1038 tweets in the past week using the rule I created.
It has collected a wide rage of interesting results and observations since Twitter is used by a community of people, who shared thoughts, information, data and ideas. Twitter allows users to post on Twitter about their personal stories or comments about ongoing social issues, celebrities or simply using it for networking as another form of social media.
I went through a few results by skimming through “Tweet Text”, from this I selected a few interesting articles that others have tweeted about the keywords I have input in to the search system.
A competition to solve the affordable housing problem in dense urban cities throughout the world: https://t.co/nBIx0FFel3
From my observation, I realised that most of the tweets are on a based on a global scale. Most of the tweet are looking at the US, UK and Asia, this is because there is a larger density in population in these areas. Most of these tweets raises question about the issue with housing affordability, to bring awareness to those who are interested in knowing more about the housing crisis.
I found most of these tweet really interesting as they have different approaches to the issues, some are more informative and explores about the causes and effects. I also observed that most of these tweets are tweeted by different government organisation, business companies and design institutions. When using the Twitter Archiver I found a non-profit organisation called Next City, with a mission to inspire social, economic and environmental change in cities through journalism and events around the world.
Web scraping was useful, but it was very difficult to filter all the result to a desirable result. I wanted to look into affordable housing in Sydney, but when searched in the Twitter Archiver some of the result were unappealing. I decided to take a step further and gone into Twitter and searched “affordable housing Sydney“, instantly it presented me with more promising results without having to scroll through the Twitter Archiver.
Although I was able to look at this issue on a global scale, I wanted to focus more on affordable housing within Australia. I explored the idea of innovative living and affordable housing that can be implemented into our busy city. With the search from Twitter, it made it a lot more convenient to browse through content since I was able to understand each article based on the headline, most of these tweets seemed to be very biased and positioned in such negative connotation. It seems like people are just tweeting and blaming others rather than confronting the problem and finding solutions to solve these issues.
The issue of asylum seekers coming to Australia is a highly debatable topic, particularly on social media platforms. Twitter in particular allows people to express their thoughts and opinion in a quick 140 character message. Using the Actor Network Theory to understand the social systems and networks surrounding the topic of asylum seekers, Twitter can also be seen as a non-humanistic actant that connects people all across the world. Human actants can read, reply and write tweets using their laptop or mobile device.
Hashtags are used for keywords/phrases to broadcast and categorise tweets, helping them to appear in Twitter searches. Advanced searches allows you to filter your results by setting specific parameters using keywords, locations, people, places and dates. This information can be exported into table format with the combined use of Google Sheets.
Over the week, I have been collecting Twitter data to show patterns of human actants and their thoughts and behaviours. I experimented with different sets of parameters to reveal more about attitudes towards asylum seekers and how they are formed and swayed. I drew on the words collected from week 4’s class task, where we were to intuitively write down 25 words that resonate with us that are often used by the media and in daily discourse about Australia’s asylum seeker/refugee issue.
I began by entering some of these words in a very broad search using the Google Sheets Twitter archive. However, I found that it was difficult to find any underlying patterns as there was an overload of data that was difficult to analyse (over 4,000 tweets). I realised that if I wanted to find data that is relevant to my specific area of interest, I needed to suggest a somewhat broad hypothesis so that I know what patterns/variables/trends I am looking for.
I hypothesised that people from the same regions may have similar attitudes. This vague assumption dictated the keywords that I entered into the Google Sheets Twitter Archive and Twitter’s Advanced Search. The Twitter archive did not allow me to specify the location of the tweets, thus I needed to manually filter through locations within Australia. I was also able to obtain qualitative data from the user descriptions, providing an insight of the underlying reasons or factors that may influence these results. I repeated this process using different keywords and compared and analysed the results. I obviously could not go through all of the results, thus I selected a random sample group of 175 people to analyse which can be seen here [https://s9.postimg.io/5gci9fw65/table_all.jpg].
From this data scraping research, my hypothesis was not entirely validated as the data showed that the vast majority of people on Twitter have similar attitudes, irregardless of where they are from. Most people tweeting about the issue of asylum seekers in Australia condone the offshore processing policies and detention centres. However, these results only reflect a tiny percentage people on twitter and an even smaller percentage of the Australia public.
In conjunction with tweet locations, I also needed to identify the attitudes/opinions presented in the tweets. I considered how one may frame their statements, focusing on how the word ‘illegal’ is used. By simply changing the linking verb ‘are’ and ‘is’, I was able to collect tweets with very different stances on the issue.
I tried to understand the background of this sample group, as that would give an insight to why they have these attitudes. From observing the bio descriptions, most people who have tweeted on the subject of asylum seekers are fairly well educated and/or are advocates for social justice.
People from CBD’s (particularly Melbourne) are a lot more vocal about asylum seeker issues on social media than people from regional areas. In saying this, one must consider that capital cities have higher populations than regional areas).
Most people who have tweeted on the subject of asylum seekers are fairly well educated and/or are advocates for social justice.
Tweets tend to focus on one specific aspect of the issue (though they are restricted to 140 characters).
Most twitter users do not have the ability to mobilise change. Rather, they act as intermediaries that retweet provocative posts made by influential mediators.
Twitter data scraping is better suited for numeric data, rather than measuring trends in opinions. I found it to be tedious as I had to manually find and organise tweets that came from locations in Australia.
A Twitterbot would ideally distinguish peoples attitudes based on how the post has been worded (i.e. ‘is illegal’ and ‘are illegal’) and match the tweets with opposite attitudes. I found that many twitter posts tended to focus on only one aspect of the issue whilst ignoring equally important concerns. This concept could lead to either a mass of heated arguments and hurling of insults, or possibly provide people with a bit of perspective and alternative insights. I would hope the latter would be the predominant outcome and hopefully force people to see the issue in a broader context.
The chosen social media platform is Twitter. I could use other social media, such as Facebook, Instagram, Blogs etc since I have not logged into Twitter for very long time however, I thought the more unfamiliar I am with ‘what is going on in Twitter’, the more interesting Twitter becomes.
Twitter is a social media that allows the users worldwide to write up their thoughts they care about only up to 140 in characters each time. It is being referred as ‘Tweet’. Twitter is commonly used for either personal or business purposes. Twitter also allows the users to follow other users with the same or different interests, follow ‘stakeholders’: actors/actresses, news agencies, companies etc, to retweet other users’ tweets, to mark other’s tweets as favorites, to reply other tweets and so on. Furthermore, Twitter allows the users to link their other social platforms with their Twitter accounts. Thus other Twitter’s users should be able to check our posts on other social media without any interruptions or having to log into those specific social media.
Through Twitter, hard-liners are able to advertise their idealised image of their beliefs as well as to express their disagreements toward their competitors non-verbally, stakeholders: companies, media agencies, governments etc compete one another by sharing their best contents in order to gain popularity measured through the number of retweets, replies and followers, general populations show their supports towards things that they care about.
The Process of Collecting Data
In order to document or collect the findings/results associated with mental health stigma within Twitter platform, the first step taken is using Twitter Archiver Google Spreadsheet to search tweets under hashtag ‘stigma’ following by keywords ‘mental health’ and ‘disorder’. Below is provided 2 flow chart graphics, which are consisted of flow chart graphic of number of Twitter users’ followers and follows, and flow chart graphic of count of tweet text. The next step taken is using Twitter ‘Advanced Search’ to search tweets under the same hashtag and same keywords however, the keywords are combined and became ‘mental health disorder’.
Comparing between the two alternative ways of collecting data of mental health stigma, I found out that Google Twitter Archiver gave me so much more structured and complete data however, Twitter Advanced Search gave me insights into what the users were actually talking about in their posts, the aim of the conversations, the tone of the conversations clearly in full design screen. In order to dig much more interesting and relevant information on mental health stigma, the best way to do that is by scrolling throughout Twitter timeline.
Here are some of the summaries of the results found:
Stigma does not always relate to mental health. Stigma does exist in a number of different areas, including community services, physical health and disabilities. When “stigma” is typed into the hashtag search column, it will show lots of irrelevant topic to mental health. Most of the topic discussed are associated with lung cancer, HIV, physical disabilities and postcode prejudice in the region.
Regardless area of issue, stigma has always been controversial and negative.
Supportive statements in association with mental health have been found. Few examples are a tweet by @MHCNSW, “Glad to see Aussie men getting on board with #ItsOkayToTalk, breaking down #stigma around #mentalhealth and #suicide” has been retweeted 4 times, a tweet by @AllanSparkes, “Speak up, stop the deathly silence.Thank U Men’s Health for helping break the stigma. @MensHealthAU #LiveStronger” has been retweeted 18 times and a tweet by @DestroyerMariko, “We’re getting there, but we still need to break the #stigma of #mentalillness. This is awful: https://t.co/TkdOWO2xow #mentalhealth #bipolar”. There are senses of relief, proud, courage and grateful throughout the words. The good thing about Twitter is it can also be used as a medium to increase awareness globally.
Current news reports have also been found. Few examples are a tweet by @Pawsitivehills, “Our Gold Sponsor Medibank Private Castle Hill are helping us fight the stigma of mental illness in the hills.” has been retweeted 5 times and a tweet by @KBoydell, “Mural art – making a difference increasing awareness decreasing stigma enhancing community relationships @themhsorg” has been retweeted twice. Twitter platform can be used to share information on current situations as well as to show their supports. However, chance is the subjects of the tweet are getting advertised. Thus Twitter platform is an alternative way to gain popularity.
One-on-one conversation tweet has also been found. A tweet by @juntei, “@helisalmiakki yeah. they aren’t seeing past stigma. maybe uncomfortable w family being MI and/or disabled? unconscious associated shame”. However, should there are concerned about privacy of personal opinions or views.
Retweet functionality is one good point. It helps the users to share good contents in much easier way however, chance is false news can spread quickly over the timeline.
Top tweets often come from users who are considered professional and interested in politics and laws, journalism, health, public speaking, human rights, psychologist and similar areas.
I managed to take another step. I adjusted slightly different keywords and hashtags: ‘stigma’ in this exact phrase column, ‘mental condition struggles uncertainty’ in any of these words column and ‘mental health’ in these hashtags column to leave out in both Twitter Advanced Search and Google Twitter Archiver in order to generate more interesting and relevant results. The current results shown up are much more relevant to the issue, including mental health stigma quotes without showing any other areas of issue but, fewer in number of results.
This post will explore the use of Twitter as a web scraper. Twitter was founded in 2006 by a small team of people (Jack Dorsey, Evan Williams, Biz Stone, and Noah Glass) and is an online social media platform for built for users to create messages of 140 characters or less as well as read them from other users on a timeline; this makes Twitter a great platform for news highlights. Twitter has been made into a very responsive website and app which can be viewed and used on all technological platforms; computers, iPads/tablets, smartphones and the Apple watch. Twitter users can only interact with other twitter users but the platform works well for images shared seamlessly from Instagram to Twitter. The main features of a tweet are outlined below.
Twitter’s re-tweet function, whereby users are able to share another users’ tweet on their own timeline, is one of the main functions available aside from actually tweeting. Similar to many other popular social media sites, Twitter incorporates a like or favourite button as well as a trending sidebar which lists the hashtags or topics that are being tweeted about the most. Direct messaging is another function on Twitter whereby instead of tweeting publicly, one can send a tweet or message privately to another user. In terms of unique qualities, Twitter was the first social media platform to incorporate the use of the now incredibly popular hashtags; by way of searching for topics and interacting with other users tweeting about that same topic. Another unique quality is Twitter’s Moments panel which provides the latest or most shared information in categories such as ‘today’, ‘news’, ‘sports’, ‘entertainment’ and ‘fun’. After a while, these moment’s articles become tailored to the individual user and the topics they interact with the most. Although Twitter limits tweets to 140 characters or less, many users live tweet or create a tweet thread with their thoughts by replying to one tweet over again to create a thread of messages.
Twitter has a very wide audience, mainly younger generations who are more frequent on social media sites, and can be accessed by anyone who can reach the site through mobile or online technology. As mentioned above, due to the character limit per tweet, many news platforms have taken to Twitter to share stories as the tweet length is perfect for news headlines for those without time to read the full story. These headlines entice people in to continue to read the rest of the story through the provided link. It is through these news stories that hashtags are generated and users continue to discuss the events often providing their own opinion. Tweets can range from news headlines to personal thoughts and inspirational quotes to including imagery such as memes, selfies, general photography and fashion – thus, almost everything can be tweeted about by almost everyone.
Other than the main purpose of tweeting, Twitter can also be used to generate large amounts of information on particular topics. By a simple word search the archiver can pull hundreds to thousands of tweets from Twitter and categorise them in a spreadsheet. Using this web scraper, I decided to inquire about the common Nigerian Prince email scam that many people receive. I firstly did a broad search on Twitter using the general search button and received the results below.
Although the initial Twitter search of the phrase ‘nigerian prince scam’ did generate many tweets on the topic, I decided to investigate further using the Twitter Archiver to ascertain if the results would be starkly different. A flow chart of the main steps is pictured below.
The search dialogue box is pictured below and the main phrase was used as well as additional words (‘email’, ‘fake’ and ‘money’) to further refine the search results. I also decided for the results to only include tweets in English as I would be unable to make sense of them in any other language.
After creating the search rule, the Twitter Archiver began to generate the results and plot them in the spreadsheet opened. Although the actual tweets in the spreadsheet were the same as those I found in the initial Twitter search, the layout of the information was majorly different. Information such as each users’ screen name, full name, tweet, the app they tweeted on were displayed as well as that users’ Twitter bio, location of the tweet and basic statistics (number of followers/follows, retweets, favourites) as shown below.
The most recent (and the majority of) tweets using this Twitter Archiver appeared to show tweets from the USA in response to a news article regarding Donald Trump and due to these in-depth results, it was then interesting to look at the Twitter bios of these people to possibly determine why they are tweeting about the topic (e.g is it a topic they are involved in in some way as many of these users were) and this led me to pinpoint two of these tweeters, and investigate their initial tweets – one of the great features of the Twitter Archiver.
Other than visualising this information in a spreadsheet, it would be very interesting to see how these tweets look on a world map. This would display where in the world each user tweeted from and in doing this one could then determine why some countries appear to discuss the issue more or less than others. Another visualisation is to display the dates of each tweet on an actual timeline as there could be many months where no-one would tweet about the topic, and some with an influx of tweets. This could be a result of government activity (as seen with the Trump tweets above) or even if there is a large amount of online email fraud occurring at one particular time.
The Twitter archiver displays the same search results as using the basic Twitter search but the layout of the information is much more in-depth (displays users’ screen name, full name, tweet, the app they tweeted on were displayed as well as that users’ Twitter bio, location of the tweet and basic statistics).
There are a variety of different ways to display the search results from the Twitter archiver including in a basic spreadsheet, on a world map (pin-pointing the location of each tweet), and displaying the tweets on a timeline (showing the when the topic was most discussed).
It was interesting to see that the majority of the tweets mentioning ‘Nigerian prince scam’ did not use the phrase in a hashtag.
Many of the tweets displayed in the results were sent as a reply to a main tweet, creating and continuing the conversation which meant the tweets were mostly in response to the same single topic. Easy to see different points of view and watch the conversation move in different directions to gauge a common public opinion.
The Twitter archiver does not enable the user to easily see and click on images/links. After completing the same search on Twitter itself, I found that the majority of tweets using this phrase did not actually include images but for the few that did, it would have been great to see them incorporated into the spreadsheet to easily map the results.
Twitter is one of most popular online social networking service that can share information, daily life and news with texts, images, and video sources. This social media platform is very easy to collect what you want to see by search the keyword and/or hashtags, which is very convenient for us to collect any data of issues. Also, the unique qualities of twitter are all of the sources are public and visible once users published it, which is easy for all of the users to communicate the topic and news at anytime, anywhere.
One of the specific function of twitter is ‘Advanced Search’. It allows users can search their needs by type the keywords in different category to help users to filter any useless data while achieving the most relevant information on the issue . For example, if you want to search ‘mental health’ as my topic, the released result will be too broad and lots of results might be irrelevant. So if users type ‘mental health’ on ‘These hashtag’ and pick a specific theme and type it on ‘all of these words’ that relate to mental health, that will give you a comprehensive result. Also, it can even be more specific if users are more strict about the dates, place and/or language.
During the web-scraping, I choose ‘sexual assault’ with the hashtag ‘mental health’ and I found lots of experience, news, with links, and critical report with videos, and images. One of article I looked it further is ‘Sexual Assault Survivors Are Using #IHaveTheRightTo as a Way to Reclaim Their Power’ written by Mckenzie Maxson. It’s about an activity with the hashtag ‘#IHaveTheRightTo’ against the phenomenon of the story from sexual assault people are being erased. In America, over than 16% women had experience with sexual assault that affects their mental health, but they can not reflect the story on the society by some reasons. So this activity is to awake people start to pay attention to this mental issue and tell the people with sexual assault that they are not alone and able to speak and share their experience.
After I read the article, I try to search the hashtag ‘#IHaveTheRightTo’ and found lots of personal tweets to support this activity and the interview with school assault survivor from Today Show. This result reflects that the function of twitter can extend the issue further with different keywords.Once we found a specific activity, it’s possible to found another activity that also relates to the topic, which means twitter might provide as many data as you want.
Another tool we could use for research is Twitter Archiver. It’s a tool that collaborates between Google Docs and Twitter. Once users sign in their twitter account in Google Docs, they are able to research by creating a Twitter Search Rule. Also, users need to consider the chosen words to research. I was just type ‘mental health’ for my first try and it brings over than ten thousand results released. So it really needs more keywords to narrow down the search result while provides users with better quality information.
One of the twitter accounts that I found it from Twitter Archiver is CWCC (Central Wyoming Counseling Center). It’s a mental health community and substance abuse treatment center to help the residents of Wyoming. Mostly, the CWCC published lots of articles that relate to mental health for users to read, and most of sources are authoritative.
One of sources I found from CWCC is the functions of treatment to different mental illness. It mentioned 90% of people suicide by mental disorder and it’s able to prevent by refining the treatment of different mental health areas. For example, Major Depression, Medications, Bipolar Disorder etc. However, therapy with patients is their position to believe that is the way to prevent mental illness.
Overall, the research functions really help me to understand much further about mental health while providing lots of inspiration for me to use it for visual design responses. At this stage, I consider to pay attention to sexual assault as my main theme and develop the graphic style with a character. I also thinking about some materials that can represent the emotions, experiences, behavior and lifestyle with sexual assault,especially ‘fear’. Also, I will consider how to use a graphic to represent treatment, no matter it’s positive or negative, but it provides a signal about treatment affects the quality of mental health services. The conversation might be the factors to reflect treatment, so I will consider using dialogue with texture and layout while insert words inside of the character that makes a strong figure and ground style to develop a contrast between external factors and thoughts of the character. The color might be black and white but depends on what kind of color are most suitable for the graphic.
5 points summary:
No matter what kind of social networking platform you chose for research, words chosen is pivotal.
In America, people with sexual assault experiences can not reflect the story on the society by some reasons.
The hashtag is the way to connect different words with a similar topic, which can help us to extend any issue or theme that you searching.
A social networking account with specific content (eg. mental health community) is the pathway to achieving what you need.
Treatment can affect the quality of mental illness by the therapy with patients.
Social media has become an integral part in the interaction of people. In a world is becoming dependent on the Internet as it is today, Twitter effectively brings people and community closer to their interests and is a great social-networking tool to update the information including daily conversations, information sharing, news critiques, and updates about an user’s life in real time without the need to read newspapers or watch television. It is also used by the very popular movie stars to connect with the audience and fans.
In order to gain a border understanding of mental health by using web scrapping data techniques such as Twitter archivers and Data Pipeline, I have documented different data sets to assist me with my research on my mental health issue. From Twitter achievers in google sheet with the hashtag #mentalhealth, the result is fetched over thousands tweets in the world and up to 500 tweets in Sydney including positive and negative tweets.
To fully understand the depth of this research, I took a closer look at mental health tweets on Twitter avandced search. I found these hashtags #mentalhealth #depression #anxiety #ChangingMinds were among the most popular hashtags related to mental health. These collected tweets contain content where the individual appears to be sincerely writing tweets about their depression and anxiety, yet some phrases may come up as negative, in the overall context, they may not actually carry a negative message. In the case of mental health tweets, they are sometimes raising awareness of the impact an illness has on people’s lives. Additionally, the imageries, quotes and linked websites are used to aim users who might be in need of psychological help. Because when a user searches a topic or a hashtag, they can be linked to a conversation with others who suffer the same difficulties and find a community that doesn’t seem to exist in the real community around them. This is especially helpfull way in the mental and behavioral health space, where not only individuals, but organizations, institutes, and departments are busy tweeting the most interesting news, positive advices and thoughts on treating and understanding mental illness
Five points summary of finding:
Twitter has been used to create outreach opportunities for those seeking help, providing information with hashtags that link to awareness and fundraising campaigns.
A social network efficiently connects those who suffer from mental illness
Raising awareness of the impact mental illness has on people’s lives.
Mental health issues are more common than you think.
Mental health conversations often go hand-in-hand with discussions about individuals mood, anxiety and substance abuse
For this exercise I decided to focus on Twitter in order to gather data about the the publics view on homelessness. Twitter, created in March 2006 by Jack Dorsey, Evan Williams, Biz Stone, and Noah Glass, is an online social networking service that enables users to send and read short 140-character messages called “tweets”. These tweets can be shared and viewed publicly or privately. Additionally users can also add hashtags that will reach a wider audience when users search that specific hashtag. Users can read and post tweets and access Twitter through the website interface, SMS or mobile device app. Other additional features have been added to enhance the users experience when it comes to text limitation. These features include the Twitter timeline, pinned Tweets, polls, mention Tweets, lists messages and cards as well as click to Tweets to extend the conversations.
Essentially Twitter is used to connect people of all ages with the same interests. It can be used as a social and professional platform where users voice their opinion, breaking news, raise awareness on social issues, business, educational tools, share their thoughts and feelings and experiences through photos or tweets.
At first I was very specific with my Twitter search which proved to not what I was expecting.
Twitter Search youth homeless social OR australia OR youth OR homeless OR smelly OR privacy OR people OR alone OR mental OR health OR depression lang:en. Most of the results had surprised me as it validated some points that I had about social exclusion.
A lot of the search consisted on LGBT related tweets confirming that there is a vast majority of youths around theworld that feel socially excluded and are homeless. Although these results were interesting, it wasn’t enough data, so I generated a new search. To continue my research I excluded LGBT to see what the results will show. Twitter Search
homeless social OR youth OR homeless OR smelly OR privacy OR people OR alone OR mental OR health -LBGT lang_en – This search interestingly enough showed reoccurring views regarding homelessness. One of which was related to the issue of refugee VS homeless citizens. Most of the tweets explored the problem that the country is facing choosing between the refugees and the homeless citizens.
Other tweets had a political view which relates to the new agreement for the US to send $38 billion to Israel.
Dr. Craig Considine – @CraigCons US govt. sends $38,000,000,000 to the Israel govt, yet this morning I walked my 3 homeless people on the way to work. This makes no sense.
Twitter search homeless “hobo ” social OR australia OR youth OR homeless OR smelly OR privacy OR people OR hobo -LGBT lang:en
Finally, drawing upon the exercise in class, we emphasised on the word hobo and its connection with the word homelessness. To further explore my research I added the word hobo to my search. I wanted to investigate what hobo means and the assumptions and different views the public holds. To start off I searched the definition of ‘hobo’. It is defined as a homeless person; a tramp or vagrant. When narrowing down my search I kept the meaning in mind and compared tweets. Most tweets referred to their physical appearance, others made fun of homeless people, lacking empathy for the homeless community.
Whereas fashion brands used the word as the title of a fashion object or reflected the the garments of a homeless person which in a way, mocks the homeless population, misleading and gives the word a new meaning in a way that society sees fit.
In the next couple of weeks I hope to not only raise awareness about homeless but also explore the desensitisation of societies perspective about homelessness. I will be creating a service design that enables the people of the public and the homeless community interact with each other to break the barriers and assumptions of society.
Twitter & twitter archiver is a great online tool to gather data and understand how a wider audience perceives a certain topic.
When researching data, sometimes the simpler the better. Specific phrases can be very limited and one must be open to explore other options which can lead to an improved result.
People’s views can be interpreted in different ways. Most of which are based on assumptions rather than facts.
Very few posts reflected peoples motivation to help the homeless community. Rather it’s all talk but no action. (Did not see any movements or protests for the homeless community).
People use the word hobo for their own benefits not knowing the true meaning behind it and lacking empathy towards the homeless community.
To research and gain insight into how mental health is portrayed, discussed and talked about on the social media platform, a web scraping exercise was undertaken on the Twitter website. It reaped interesting results and observations as Twitter is used by people with different objectives. These include individuals who choose to post on twitter their personal stories or comments about ongoing social issues, some celebrities or ‘officials’ choose it to portray only the positive sides of themselves or some as a way of simply using it as an easy network to connect with people and follow (or sometimes cyber stalk) other people.
To start with a general idea first, my twitter rule using the google sheets web scraper was the words ‘mental health’ written in English, anywhere in the world. The results were immense – up to 10,000 before I had to stop incase the page crashed. Despite the overflow, I did find some interesting posts and how mental health is talked about.
Some were supportive organisations or individuals posting links, photos and other sources to help raise awareness of mental health which then people chose to retweet or comment on. These friendly and approachable tweets were easily found but the tweets I found most interesting were personal, raw and upfront tweets from individuals expressing themselves and their experience or opinion about mental health. To find these more personal stories, I added a rule to find tweets including the hashtag #mentalhealth.
*These posts which were possible to see as their account was on ‘public’ allowing anyone to be able to see what they post.
Going through these findings, it was noticeable that there were much more ‘organisations’ or ‘support pages’ posting links and photos than individuals who comment on their opinion or share their experience with the mental health issue which still highlights the big possibility of the stigma and taboo nature of mental health.
The social media source that I’ve chosen to explore in this web scraper is twitter. As I’m sure you will now know, Twitter is a platform that enables the user to read and post 140-character messages, photos and videos. In this format, Twitter amplifies the nature of 24/7 media. The reactionary nature of social media serves to speed up the cycle of reporting and opinions. Hash tags and trending subjects both reflect traditional media and generate organic content.
It’s is a platform that enables the user to read and post 140-character messages, photos and videos. Since its inception in 2006, Twitter has evolved into a platform that fosters political engagement and discussion from a grassroots level, giving a voice to ordinary people and breaking down traditional barriers of entry to publication and media. The accessibility of Twitter is also what makes this platform a valuable resource for marginalised groups of people to push policies and engage in politics in ways that they were unable to do prior.
Finding Humanity in Data
With this unique feature in mind I aimed to explore how refugees on Manus Island and Nauru were using the platform to express their views, interests and emotions.
I began doing this by using a Google chrome add-on that archives the history of a particular hash tags –Twitter Archive. I looked up the hash tags #bringthemhere and #letthemstay, the current trending hash tags in Australia used for refugee issues.
In the initial stages of this scrape I looked at how much the content of tweets were shaped by their context, by looking for hash tag patterns in geographic location. However, as this progressed I realised that I was shifting the focus onto the Australian population and away from the refugees. To accompany for this, I realised that maybe I was scraping for the wrong type of data and I needed to focus on a more abstract type of data to render the type of results I wanted.
Whilst my search for relevant data in this focus area was fruitless, I found an account which was repeatedly showing up with and IP address from Papua New Guinea. When I clicked on the hyperlink it took me to the page of a 25 year old Iraqi refugee.
When I visited the page, I was invited to follow other refugees who were on twitter and talking about their time and experiences in offshore immigration detention centres.
I documented a selection of posts on each profile which were the most popular via retweet or favoriting. The results of these indicate that twitter users were more responsive to tweets that was organic and original in content and / or personal opinion and/ or personalised through use of emoticons. It was these results that prompted my interest in the use of language and expression as a form of data.
Of these profiles I ran an analytics program through to see which words were most common on each of the profiles, what were the most used hashtags, and what time of day they were each posting at.
The results are indicative of the humanity of people in detention; each user has an individual mode of self-expression. This subjectivity of refugees is often erased in the media, which tends to depersonalise refugees and thereby strip them of their identity. Looking at the analytics of these results provide insight to the similarities and differences between the accounts and highlighted the individuality of each refugee as it would for an ordinary person.
As the nature of my research has been predominantly towards representation of refugees in the media vs the media generated by refugees it would be interesting to explore avenues in which I could emphasise the humanity and ordinariness of refugees.
A manner in which I think this could be most effective is by considering the opposite spectrums of similar situations, comparing the spaces of Australian suburbia with Nauru and Manus. In a brainstorm of ways I could do this is looking at physical items like objects, people, space, and abstract items like dreams, ideas, language and feelings.
Image: Wallman, S. A Guard’s Story, 2014
With the development of technology, Social Media becomes an indispensable element of daily life, it brings together the news, trends and best practices around enterprise social and digital marketing, and provide people opportunities to empower themselves and share their thoughts in an open public platform. Twitter is one of the most popular online social networking services nowadays, it allows user to send and read ‘Tweets’, which are messages of up to 140 characters that can contain images, video and web links. Twitter has described as ‘the SMS of the Internet’ because the user can always access Twitter through SMS as well as website interface and mobile device application. Beyond that, every user’s information and tweets are forced to be public, registered users can read and post tweets, on the other hand, unregistered user can only read tweets. Because of these, News on Twitter breaking faster than anywhere else. Until March 2016, Twitter has more than 310 million monthly active users.
Believe it or not, Twitter has become one of the most powerful database systems at present. Recently, I have been working on the issue of Obesity and Healthy Living, specific in food industry and fast food advertisement. To push my research forward and to insight more people’s thought and response, I did an advance research on Twitter with a number of keywords included ‘fast food’ with ‘ad’/ ‘advertisement’/ ‘advertising’/ ‘packaging’/ ‘design’ / ‘industry’. All data must be written in English only, and can from anywhere in the world. The data were collected from 25 August to 3 September. The map below is the flow chart of my research process.
Within this web scraping practice, I gained a total of 607 Tweets in the result. It is obvious to see the fast food advertisement and the fast food industry has become a very popular topic nowadays. Most of the outcomes are negative, I can see people are not happy with the strong and successful fast food industry nowadays. The GIF below is the outcome of my research.
I summarised the outcome into five key points with a few relevant Tweets.
1. The fast food industry spent a lot of money on advertising and made themselves looks great, however some of the advertisement which aim at children might have some negative influence on children
“#locad_interesting Fast food companies in the U.S. spent about $2 billion in #advertising in 2015. #marketing #media https://t.co/nX2BwZbLHa” – @LocadSmm
“RT @youngevity: “Last year the fast food industry spent over $4 billion on advertising. Now that’s some #food for thought.” https://t.co/Py…” – @Morgantlvory
“The U.S fast-food industry spends approximately $1.6Billion each year on marketing aimed at children -Federal Trade Commission” -@FitnessRetweets
“@ChangeMillieu I sick of big fast food franchises advertising to kids on tv for a start. They get a 100% write off. Tax payer funded adverts” – @CashAnonymous
“As I see it, fast food outfits have targeted small children with their advertising in a ve #AnthonyBourdain #quotes https://t.co/HHGPDwuljN” – @RussoRussel
It is obvious to see the excessive fast food advertisement has become a worldwide issue, fast food advertisements are anywhere in social media. People are complaining fast food companies spend too much money on advertising. Beyond that, the fast food companies also target children by providing them discounts, such as buy one get one free, and toys for children. Because of that, more and more people are worrying these might affect children’s eating habit. The influence of fast food advertisement in children’s eating habit has become is a very popular topic a long while. These tweets above has been retweeted many times.
2. Lots of people hate fast food industry, especially for those advertised by celebrities.
“@andresless how do you kindly say “fuck you and your fast food industry?”” -@whos_eva
“@Jemmyjems_ the fast food industry is being corrupted by haram, so sad” -@Jeregyptan
“RT @myhairisblue: ME: the fast food industry is a systematic plague on our society MY CRUSH: i’m hungry for fries ME: https://t.co/zgiTDxEo…” -@karenstein_bear
“I grew up on McDonald’s – didn’t make the connection. The fast food industry are deceptive murderers. https://t.co/NP990cNHGh” – @TereAlbanese
“#Advertising What a single Tweet from kanyewest did for McDonalds https://t.co/Mb0xZyZY61 — Twitter Advertising (TwitterAds) September 2…” -@Advertis_1ng
Lots of people expressed their abominated and discontented about their thought of fast food industry on Twitter. Some of their attitudes were a little bit rude, they use the F-word on their Tweets to fast food industry. Furthermore, people are not happy with celebrities to preach up the fast food branding. Kanye West apparently random Twitter proclamation that his favourite brand is McDonald, which has been retweeted many times. It is undeniable that could be a great marketing moment for the fast food chain. A research showed that McDonald’s Twitter mentions went up 900% from the hour before Kanye sent out his proclamation to the hour after. However, lots of people were retweeted with a negative comment. People don’t like celebrities advertising fast food branding.
3. Some fast food advertisements seem not that appropriate.
“I know it sounds a bit pedantic but its odd to me that Panthers are advertising cheap fast food when they’re a sport team? #Fatpanthers” -@laura4m11
I completely agree with this tweet. A sports team should not represent to a fast food branding as these could mislead lots of people, especially teenage and young adults and affect their eating habit. In fact, this kind of situation is happening all around the world, this has been shown through the Australian Olympic delegation is representing McDonald; and a very popular sport racing TV show in China called ‘The Amazing Race’ is also representing McDonalds. This is NOT appropriate！
4. Some people think more policies need to be made to strict the fast food advertisement.
“If advertising for cigarettes is not appropriate then why is advertising for chocolates, chips, fast food etc appropriate” -@feelcheatedcom
There are a few people are proposing this issue recently. For instance, Sam Ikin, an online producer for ABC News Digital based in Hobart wrote a news article about this issue over a month ago. Lots of people agreed with Sam, however, the related government departments still NOT pay close attention of this proposal yet. This has disappointed lots of people.
5. Some people think government need to make some measures to support healthy fast food advertisement, while in fact not that many people care about it.
“RT @VLubev: Agree with Corbyn on fast food. We should support healthy fast food, as Corbyn has supported the Kebab Industry Awards. #Salad” -@thatsmabhoy
It is obvious to see the lack of regulation in fast food advertisement. Under the circumstances, supporting healthy fast food industry and advertisement could be a good way to correct people’s eating habit and reduce the obesity rate. Unfortunately, this issue is NOT in popular demand, this has been shown through a ZERO retweet of the tweet.
After the advance research, I gained plenty valuable data and information about people’s thought and response of the fast food industry and the fast food advertisement. Before I am done this web scraping research, I was thinking to design a proposal that against the fast food branding. However, there were already too many articles and news about this issue and I think people already aware the harm of eating fast food. Therefore, for my future visual design, I would like to design a proposal to support the healthy fast food industry.
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