Post 6 by Alice Stollery
Web Scraping Tools
Twitter has become both a social and professional platform allowing dissemination of anything from random thoughts, ignorance and pointless memes to breaking news and public opinion. With 313 million active global users recording and sharing their thoughts, feelings and experiences, twitter is a real-time source of information and has become a medium in which people can keep up with those they know and those they don’t. The 140-character limitation on posts makes it a perfect tool for researching public opinion and scraping the platform for data, without being overwhelmed.
Web Scraping Process
Twitter archiver was my main source when scraping the web, as it allows you to create a search rule and, unlike other tools such as twitter advanced search or facebook, it collates the results into a single excel spreadsheet. This feature became invaluable in gaining insights from the results as it allowed me to sift through the information using specific terms, further refining my results. Insights often came as a result of tangents, originating in previous search results.
Not being a twitter user myself, I stumbled at the start. I created a twitter account and downloaded twitter archiver and linked it to my gmail account. Initially, I ran searches that were very specific, in an attempt to find information on stereotyping and the technological divide within homelessness. I set the location to Sydney, however, this failed as I did not receive a single result. So, I changed the location and widened the search to Australia. Again, I was being too specific with the number of words I was using and twitter archiver came back with nothing.
I then decided to change my approach and create a simple search of ‘homeless’ and ‘stereotyping’ which produced 14, 931 results. I then used the search tool to search for key words from the group issue mapping class within the data set. This helped me to break down the vast amount of information and to see what people were saying about issues within homelessness. This became a very interesting process. The initial words I searched within the results were quite basic as I was still getting use to using the software. However, they still provided insights and I have listed the most frequent words below.
Interestingly, homeless men were referred to 4,279 times compared to only 251 times for homeless women. This is in line with the statistics revealed through one of my earlier posts that stated 82% of Sydney’s homeless are male and only 17% are female. As these posts were not Sydney specific, it is interesting to see that these statistics may also be a good indication of the situation in other parts of the world. I am curious as to why the numbers of homelessness between sexes differs so significantly. And can begin to understand why previous sources outlined a lack of services tailored to homeless women. I can only assume that this is due to less demand for them..? I could look further into the specific causes of male homelessness, contrast it to female homelessness and see if there is an opportunity to intervene with my design response.
Another observation was that the word ‘help’ only appeared in 1,137 posts out of 15,000. Looking further into these posts I found, ‘help’ took different forms. Some were genuine posts about helping the homeless with information of individuals lending a hand or community projects. It was really interesting to see how others were approaching and tackling the issue on a personal level.
Other twitter users made genuine offers help. In his case twitter was used as a form of communication to reach people in a particular area to help assist in helping those in need. This highlighted the possibility of technological responses that act as a connection between those with something to offer and those in need. Service design could bridge the gap between say, small businesses with food or accomodation available and the homeless.
While each of the above insights were interesting, perhaps the most interesting insight was not only the presence of stigma in these posts but stigma as a result of the frequent misuse of the word homeless. While there were 15,000 posts that included the word homeless, I found that more often than not people were using it as part of casual conversation, to describe their lack of dress sense or effort invested in the appearance of friends or celebrities.
It seems that, no matter how hard I try to broaden my understanding of the issue, I always seem to arrive back at stigma, perceptions of the homeless and ignorance towards their circumstances.After seeing these tweets, I decided to look into how the misuse of language is hindering our ability to tackle homelessness. As a result, I then ran a search on the term hobo to understand how often it was being used within the twitter sphere. This search found 10,732 results with the term hobo included in it.
“RT @emmaabel_: I am going to try and make myself look decent tmmr and not like a hobo”
“@Lizbeth_923: First Date And I Look Like A Hobo 🙂”
I believe the ubiquitous misuse of language surrounding homelessness is dehumanising the homeless, and ultimately, taking away from the issue. This is not only present in social conversations and personal online interactions, but is also reinforced by the fashion industry as seen below with the release of the ‘hobo’ bag.
Shockingly, the term hobo is also being used as a design response to the issue, evident in the iHobo app. The virtual pet app that puts a homeless person in your pocket for you to feed and take care of. Forget to feed your hobo and he dies or runs off to get drugs. I think using gaming to tackle peoples perceptions is an interesting idea but I can’t help but feel this approach is distasteful, to say the least, and is reinforcing the publics stereotypical and often negative perceptions on the issue, not to mention dehumanising those in need.
To further investigate this area, I could also try searching the term ‘tramp’ and other common names used to describe the homeless. The above tweets highlight a severe lack of empathy among the general population for those suffering from homelessness. Homelessness does not seem to be a topic that people are talking passionately about. The homeless have fallen by the wayside and we are all so desensitised to the issue that homelessness has become commonplace in daily language for all the wrong reasons.
Design responses could enable change in this area, and I would like to focus on the role language plays in the issue. I think there are already a number of individuals and NGO’s working to directly help the homeless so I would like to instead create a design response that tackles the wider issue and aims to influence the views people have of the homeless. Tackling this wider problem of perception, assumptions and language would aim to influence the common vernacular rather than direct action on a smaller scale. This would hopefully result in a knock on effect, creating empathy and engagement among the wider population, to ultimately generate positive outcomes on a wider scale.
In terms of it’s form, I could create a twitter bot that calls people out on their misuse of particular words. However I think this would breed hostility rather than empathy. Language will be an important element and in order to generate a feeling of empathy I think the design would be suited to a poetic response that encompasses feelings and a the contradiction of meanings.
In an attempt to reveal relationships between language and the the number of homeless people, I could visualise the frequency of misuse of vital words. Perhaps I could plot the locations of these misuses and correlate this data with the number of homeless people in that particular area to see the relationships between the two and to discover how local attitudes affect the issue. However I am hesitant to do that as I do not think data will generate an empathetic response in the way that I am hoping.
Five Point Summary
- Twitter and twitter archiver are both very effective tools in scraping the web for data to understand how the wider population are feeling towards homelessness.
- It is important to remain open to outcomes outside your initial understanding. I went into this process with a focus on stigma and the technological divide, yet ended up delving further into the role language plays in creating barriers to a solution.
- Those offering food, services or accommodation on a personal level have great difficulty in finding the right people to help. Perhaps a service could be designed to bridge this disconnect.
- More often that not, conversation around homelessness is not referring to the issue at all and is used more so in casual conversation to describe appearances. The misuse of homeless terminology is rife among the online community and has seeped into the common vernacular ultimately resulting in a lack of empathy towards sufferers.
- Homelessness does not seem to be a topic that people are talking passionately about. The homeless have fallen by the wayside and we are all so desensitised to the issue that homelessness has become commonplace in daily language for all the wrong reasons.
AlaynaaaMarie. 2016, ‘I just added this to my closet on Poshmark: Michael Kors Julian Chain Exotic Hobo’, Twitter post, 2 September, viewed 2 September 2016,https://twitter.com/AlaynaaaMarie/status/771826144198873088>.
Alex. 2016, ‘I look like a hobo walking to class but idgaf’, Twitter post, 1 September, viewed 2 September 2016,< https://twitter.com/alexj0ness/status/771405203354550272>.
Bryant, M. 2010, iHobo app puts a homeless man in your pocket, The Next Web, viewed 31 August 2016,<http://thenextweb.com/apps/2010/05/10/ihobo-app-puts-a-homeless-man-in-your-pocket/#gref>.
Dan Kenny, 2016. ‘Can’t think of any but I’ll happily help give it out in the streets after service, be done 11:30ish’, Twitter post, 2 September, 2 September 2016,< https://twitter.com/DanKenny29/status/771812145558347779>.
Dream Closet. 2016, ‘Always torn between getting ready and looking cute or being lazy and looking like a homeless person’, Twitter post, 2 September, viewed 2 September 2016,< https://twitter.com/TheDreamCIoset/status/771684469287845892>.
Emmabel. 2016, ‘I am going to try and make myself look decent tmmr and not like a hobo’, Twitter post, 1 September, viewed 2 September 2016,<https://twitter.com/emmaabel_/status/771505054201241600>.
EWapo. 2014, ‘I’m playing a game called iHobo where you look after a tramp and I’m legit checking up on him every 5 minutes, I’m here for you trampy’, Twitter post, 10 January, viewed 3 September 2016,<https://twitter.com/EWapo/status/421761288617091072>.
FryGuy84. 2015, ‘who remembers that app iHobo when you had to look after a tramp like a tamagotchi’, Twitter post, 29 January, viewed 3 September 2016,< https://twitter.com/fryguy84/status/560837097612140545>.
Hungry Hoss, 2016. ‘At a wedding in Brighton & there’s a shit load of food left over… does anyone know a homeless shelter or similar who’d make use?’, Twitter post, 2 September, viewed 2 September 2016,<https://twitter.com/HungryHoss/status/771804636298809345>.
Lizbeth_936. 2016, ‘First date and I look like a hobo’, Twitter post, 1 September, viewed 2 September 2016,< https://twitter.com/Lizbeth_936/status/771517651130601472>.
NinjaRonnie. 2013, ‘Shit I forgot about my hobo’, Twitter post, 9 June, viewed 2 September 2016,< https://twitter.com/NinjaRonnie/status/343751075221872640>.
Pissedoff99. 2016, ‘You ignorant description kind of shows how insensitive people involved with fashion industry can be with social issues’, Twitter post, 3 September, viewed 3 September 2016,<https://twitter.com/PissedOff99/status/771977859829202944>.
Prank Videos, 2016. ‘He gave his ‘winning’ lottery ticket to a homeless man, gave the store owner the money to pretend its real, amazing’, Twitter post, 11 March, viewed 3 September 2016,< https://twitter.com/30SecPranks/status/708500013349900288>.
Smith, C. 2016, Twitter Statistics and Facts, DMR Stats and Gadgets, viewed 2 September 2016,<http://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/>.