As a structure for the brainstorming exercise discussed in post 8, we divided our concepts into the different fields of emergent practices – service design, generative design and data visualisation. This was helpful in stimulating further ideas, by pushing us to consider alternative ways of approaching the problem space we had sketched for our issue area – asylum seekers and refugees. However, it also showed that there were some blurry lines between these fields, with some of the ideas we talked about potentially fitting into more than one category.
Another tool we used to guide the discussion was to carry forward a system we created during our collaborative research phase. When sharing information, we created a system of hashtags related to each group member’s areas of focus, that would identify the key aspect of the issue to which a particular source pertained. For example, we used #resettlement, #attitudes and #mentalhealth. As we got into the problem space definition, we then brought these categories – along with the relative expertise that each person had built in their focus areas – into defining key aspects of our issues’ problem. We also brought to bear the findings and reflections from the recent issue mapping exercises that identified key polemics within our issue.
The key strength of the brainstorming exercise was that we were able to generate a wider latitude of concepts and interrogate them to immediately make them sharper and more relevant by discussing them with a small group, rather than ideating individually. At the same time, working with just a few people was likely more productive than trying to collect and document an ideation process with a large group of 10, 20 or more. On the other hand, despite a large body of research between our group members collectively, not a lot of the research we had each done focused on recent solutions, campaigns or tools and I think we faced a weakness in potentially replicating existing projects and not being able to apply learnings from a deep analysis of existing design responses.
As I sat down in class with my issues group, I knew the inevitable was going to happen. As the butchers paper was yet again spread around the class, my eyes watered. The thought of mapping again put my head in a spin. But, to my surprise, we weren’t mapping. We were brainstorming….which is basically the same thing as mapping.
So, in our issue groups, we helped each other brainstorm possible design responses for each persons particular topic. This exercise helped flesh out my problem statement from earlier in class and also my specific area of interest within mental health, which I have explained further in POST 8. They suggested even more specific areas within my topic, possible conceptual ideas, and current design responses and directions that relate to my issue. This collective brainstorming discussion on each persons topic helped create new perspectives and directions for possible service, generative or data driven design responses.
The first brainstorm was on my problem statement, which I further explain in POST 8. My group and I discussed simplifying communication between patient and doctor in order to create a more comfortable dialogue. One of my group members also directed me to a current medical design response called Babybe which helps regulate the heartbeat of babies. This created another direction of providing care and guidance outside of healthcare for people suffering with mental health issues. This brainstorm provided me with a few avenues to delve into with possible design solutions.
The second brainstorm was based on the problem ownership and control of our state of mind. We collectively brainstormed ideas about analysis of habits and feelings experienced throughout a day, changing perspectives on situations and the importance of mindfulness. In this brainstorm, we began to break off our ideas into the areas of service, generative systems and data visualisation designs. We got more into the process with this mind-map and generated more ideas and discussion.
Our third brainstorm was based around the idea of proactive self help and mental wellbeing. Again, we categorised ideas into the three emergent practice areas at the bottom of the brainstorm. We came up with ideas such as a self help system/ tool kit, motivation diary and a happy graph. Interestingly, this brainstorm incorporated some drawing and sketching as well to better communicate ideas.
If you have been reading my blog posts consistently, you would know my view of mind mapping and brainstorming quite clearly by now. I find brainstorming in a group has its ups and downs. I found it helpful in fleshing out my specific topic and problem statement but when it came to actually brainstorming ideas, we often got stuck or went off track in our discussions. I also think I needed a better understanding of the three types of emergent practices (service design, generative systems and data visualisation) before mind-mapping ideas as I felt like I was flying blind. I think group brainstorming is a great starting point for creating ideas and gaining fresh insights, but it is ultimately always up to the individual to create a final design response.
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.