Schneier on Security

vas pup • February 1, 2018 10:20 AM

@all: you could analyze mental health problems of military personal as well – see below

Clues to the state of your mental health may be hiding in plain sight – in the tweets you send and the Facebook updates you post:

http://www.bbc.com/future/story/20180201-how-your-social-media-betrays-your-mood

“There it is in your Facebook timeline or Instagram gallery – a digital footprint of your mental health.
It’s not hidden in the obvious parts: the emojis, hashtags and inspirational quotes. Instead, it lurks in subtler signs that, unbeknownst to you, may provide a diagnosis as accurate as a doctor’s blood pressure cuff or heart rate monitor.
It also means the platform has important – and potentially life-saving – potential. In the US alone, there is one death by suicide every 13 minutes. Despite this, our ability to predict suicidal thoughts and behavior has not materially improved across 50 years of research. Forecasting an episode of psychosis or emerging depression can be equally challenging.
But data mining and machine learning are transforming this landscape by extracting signals from dizzying amounts of granular data on social media. These methods already have tracked and predicted flu outbreaks. Now, it’s the turn of mental health.
Studies have found that if you have depression, your Instagram feed is more likely to feature bluer, greyer, and darker photos with fewer faces. They’ll probably receive fewer likes (but more comments). Chances are you’ll prefer the Inkwell filter which converts color images to black and white, rather than the Valencia one which lightens them.
Researchers from Harvard University and the University of Vermont used these techniques in their recent analysis of almost 44,000 Instagram posts. Their resulting models correctly identified 70% of all users with depression. compared to a rate of 42% from general practitioners. They also had fewer false positives (although this figure drew from a separate population, so may be an unfair comparison). Depressive signals were evident in users’ feeds even before a formal diagnosis from psychiatrists – making Instagram an early warning system of sorts.
Meanwhile, psychiatrists have long linked language and mental health, listening for the disjointed and tangential speech of schizophrenia or the increased use of first-person singular pronouns of depression. For an updated take, type your Twitter handle into AnalyzeWords. It’s a free text analysis tool which focuses on junk words (pronouns, articles, prepositions) to assess emotional and thinking styles. From my 1017 most recent words on Twitter, I’m apparently average for being angry and worried but below average on being upbeat – I have been pretty pessimistic about the state of the world recently. Enter @realdonaldtrump into AnalzyeWords and you’ll see he scores highly on having an upbeat emotional style, and is less likely than average to be worried, angry, and depressed.
researchers at Harvard University, Stanford University and the University of Vermont extracted a wider range of features (mood, language and context) from almost 280,000 tweets. The resulting computational model scored highly on identifying users with depression; it also was correct in about nine of every 10 PTSD predictions.
Reservations persist more broadly in this field, though, especially around privacy. What if digital traces of your mental health become visible to all? You might be targeted by pharmaceutical companies or face discrimination from employers and insurers. In addition, some of these types of projects aren’t subject to the rigorous ethical oversight of clinical trials. Users are frequently unaware their data has been mined. As privacy and internet ethics scholar Michael Zimmer once explained, “just because personal information is made available in some fashion on a social network, does not mean it is fair game for capture and release to all”.
Data mining and machine learning offer the potential for earlier identification of mental health conditions. Currently, the time from onset of depression to contact with a treatment provider is six to eight years; for anxiety, it’s nine to 23 years. In turn, hopefully we’ll see better outcomes. Two billion users engage with social media regularly – these are signals with scalability. As Mark Zuckerberg wrote recently while outlining Facebook’s AI plans, “there have been terribly tragic events – like suicides, some live streamed – that perhaps could have been prevented if someone had realized what was happening and reported them sooner.”
Mental health exists between clinic appointments. It ebbs and flows in real time. It lives in posts and pictures and tweets. Perhaps prediction, diagnosis and healing should live there, too.”