Having undergone a substantial amount of research into the dialogue around mental health in more traditional formats, such as scholarly journals and newspaper journalism, this iterative process moved on to the analysis of data generated online, particularly through the social media platform Twitter.
For this exercise I have chosen to focus on Twitter over other social media platforms, as I was particularly interested in people’s responses to those who actively voiced their support for mental health sufferers or shared their personal experiences. Twitter is a social media platform that allows users the world over to freely share updates from their life, provide insights into their interests and values, and become connected with communities of like-minded individuals. The premise of the platform is that a single message is limited entirely to a total of 140 characters. By enforcing such minimal statements, users are forced to engage with language in different forms in order to convey their message. Alternative means included emojis, hashtags, acronyms, memes and pictures of larger bodies of text.
Through our weekly group discussion it is evident that mental health is an endlessly broad topic due to its innate complexities and myriad of consequences. Thus, when first approaching the fathomless depths of data generated via Twitter I was unsure of how to effectively define my search parameters. As a starting point, I utilised the web scraper Twitter Archiver to simply generate a spreadsheet of any tweet containing the terms ‘mental’ and ‘health’. Understandably, this resulted in the collation of well over a thousand tweets from the past few days. From here, I began to experiment with a wide variety of search term combinations in order to gain a brief, summarised insight into the manner in which mental health is discussed.
To try and find responses that could be considered more relevant to my context, I limited each of my Twitter Archiver search rules in turn to those generated in Australia. This greatly limited the results garnered, as the profile location of a user needed to be ‘Australia’ precisely, not ‘Aus’ or ‘Sydney’ or ‘Northern Territory’. By using Twitter Archiver I found that many individuals choose not to disclose their location or may use fictional or nonsensical locations, such as ‘everywhere’, ‘stuck in a constant nightmare’ and ‘99% bed, 1% sofa’. The need for specificity, along with users being under no obligation to accurately identify their location, means this criterion cannot be utilised as a reliable filter of data.
It was through constantly experimenting with the exact combinations and qualifications of search terms that I eventually arrived at a focal point upon which I could develop a more sound understanding of the discussion taking place.
Being a user of various social media platforms, if not Twitter specifically, I begun this process with an expectation of reading scores of hateful, biased and uninformed statements, particularly when specifically searching for tweets about mental health, which continues to be surrounded by stigma and is even regarded as a taboo topic of discussion. Across the various search terms and hashtags it was clearly evident that for every remark, whether opinion or fact, there would be another that contradicts, admonishes, reasonably argues or demeans it. This occurred regardless of that person’s standpoint. No opinion is universal and Twitter provides an outlet for anyone who is willing to voice theirs.
It became apparent that social media platforms simultaneously create amazingly supportive spaces and disgustingly vitriolic and vulgar spaces. The relative anonymity of online discussion seems to prompt many users to disregard all sense of human decency and respect to promote their own thinking. Whether users truly believe in the values they are espousing online is not guaranteed, but in the realm of mental health, such comments can have an incredibly significant impact upon the stability and comfort of the individual.
Online platforms such as Twitter offer an, as yet, historically unparalleled capability for people across the globe to share their stories and exhibit empathy. Rather than quietly disagree, or even begin an open and thoughtful discussion, many individuals choose to utilise the relative anonymity of the web to voice all manner of proclamations, no matter the absurdity or cruelty. From my web scraping exercises it became apparent that controversial topics are a particularly nasty breeding ground for hate speech and incendiary comments. When commenting on the inappropriateness of newly trending #IGetDepressedWhen, @ItsFeminism received an onslaught of hateful replies, few of which chose to reasonably debate the defined distinction in the term ‘depressed’ versus ‘depression’. One particularly disgusting response simply stated “when you’re making my sandwich don’t put mayonnaise on it please”.
An avenue I had not considered when refining my search terms was to experiment with limiting my search rules to only gather tweets that included @. Considering the focus of my web scraping has been on the dialogue around mental health, this could have generated an archive of rich discussions rather than simply a collection of single statements.
Potential Design Responses
– A digital flipbook of all tweets mentioning a set list of mental health terms could continuously click over on an external website. From first loading the page, users are given a minute to watch the posts sourced from social media. With each post there is an option to ‘Reply’, ‘Retweet’ and ‘Like’. There would also be the option to ignore, simply by allowing the tweet to pass without interaction. At the end of the allotted time their choices are collated and presented, ending in a short statement of their perceived values based on these results. There could also be an element of education as the conclusion of the piece. Somehow, medical definitions, along personal accounts from stakeholders from sufferers, family members and partners to nurses, doctors, community leaders, could be incorporated into the site. This would hopefully dissuade stigmas and put a relatable and human face to the oft repeated ‘1 in 4’ statistics and detached infographics.
– A flipbook could be generated in both zine and digital form that critically compares the flippant statements of individuals with the profound and emotive experiences of those with mental health issues. The purpose of which would be to prompt open, honest and respectful discussion of the stigmas surrounding mental health. Language could be similar to that of old (1950s) advertisements wherein the audience is typically condescended to. To further promote the sense of outdated ideologies, the aesthetic could be reminiscent of old analogue flip clocks.
– It may also be interesting to hang three long strips of butcher’s paper side by side on walls in public spaces. One would begin ‘#IGetDepressedWhen’ and the other would start with ‘#DepressionMeans’. The premise would be for people to fill in their responses to the two statements. A third poster would be composed of facts about depression, its impacts on people’s lives, the true role of medication in its treatment, along with statements that disprove various prominent stigmas associated with the illness. People would be encouraged to upload photographs of the posters to social media under both hashtags to promote further discussion and awareness of the issue. These images would be taken from social media and presented together on a generative site where users can also directly upload their responses.
Possible stigmas to focus on could be: mental health and crime; depressed and depression; medication for ones mental health; mental health and work (are they safe/reliable/trustworthy); mental health and the family unit (unfit parents).
honestjenjen 2016a, ‘#IGetDepressedWhen Hey guys I have Generalized Anxiety Disorder, so no one else can ever say they’re “anxious” now, kthx’, Twitter post, 3 September, viewed 4 September 2016, <https://twitter.com/honestjenjen/status/772117783970668544>
honestjenjen 2016b, ‘#IGetDepressedWhen peeps on Twitter are so stupid that they don’t realize that words such as “depressed” can have more than one meaning.’, Twitter post, 3 September, viewed 4 September 2016, <https://twitter.com/honestjenjen/status/772117462582112257>
honestjenjen 2016c, ‘#IGetDepressedWhen people take hashtags too seriously. Seriously wtf everyone gets depressed. Its not ONLY a mental illness. #WhySoSerious’, Twitter post, 3 September, viewed 4 September 2016, <https://twitter.com/honestjenjen/status/772113550638587904>
ItsFeminism 2016, ‘#IGetDepressedWhen I have clinical depression which is a serious mental disorder and not a trend.’, Twitter post, 31 August, viewed 4 September 2016, <https://twitter.com/ltsFeminism/status/771124461101809664>
ItsSkullss 2016, ‘@ltsFeminism aww boo hoo, stop crying about it over Twitter where you are literally asking for hate. Pity pity pity.’ Twitter post, 31 August, viewed 4 September, <https://twitter.com/ItsSkullss/status/771220624865841154>
Mau5ful 2016, ‘@femaletitan2 @ltsFeminism when you’re making my sandwich don’t put mayonnaise on it please’, Twitter post, 1 September, viewed 4 September 2-16, <https://twitter.com/Mau5ful/status/771415540141268993>
Nyer, D. 2016, ‘@TheBloggess #IGetDepressedWhen is trending? Awesome! I can’t wait till #IGetCancerWhen & #IGetMultipleSclerosisWhen start trending too!’, Twitter post, 31 August, viewed 4 September 2016, <https://twitter.com/deenatypedthis/status/771076203293716480>
Raimundo, A. 2016a, ‘The #IGetDepressedWhen take over is awesome – what started as a stigmatizing hashtag has ended in an education. Depressed is not a mood.’, Twitter post, 3 September, viewed 4 September 2016, <https://twitter.com/asraimun/status/772302326929338368>
Raimundo, A. 2016b, ‘#IGetDepressedWhen neurotransmitters are unbalanced. I use therapies + medication 4 balance. It happens even when everything is awesome’, Twitter post, 3 September, viewed 4 September 2016, <https://twitter.com/asraimun/status/772301840729853952>
Spencer, T. 2016, ‘@ItsFeminism [screenshot of the dictionary definition of depressed]’, Twitter post, 31 August, viewed 4 September 2016, <https://twitter.com/tmsp2003/status/771141196123033600>
TheBloggess 2016, ‘#IGetDepressedWhen I run out of my selective serotonin and norepinephrine reuptake inhibitor. I may be taking this hashtag too literally.’, Twitter post, 31 August, viewed 4 September 2016, <https://twitter.com/TheBloggess/status/771036106825478149>
Twitter 2016, What’s Happening, Twitter, San Francisco, viewed 6 September 2016, <https://about.twitter.com/company>
Wilson, S. 2016, ‘@ltsFeminism so only people with clinical depression are allowed to talk about getting depressed? Other people get depressed too.’, Twitter post, 31 August, viewed 4 September 2016, <https://twitter.com/Swilsonn36/status/771172528928624640>
– Alexandra Macoustra