Raw

Or: mental health monitoring via social net activity textual analysis. ELIZA was a clunky 1960's AI application, mimicked a psychotherapist quite convincingly via crude scripts (there are loads of online versions). Nearly 50 years on, surely we can do better?

I've no idea what work might have been done on this already, got other things to do right now, so just a note to come back to or for someone else to look into.

From a tech point of view at least, the first step to fixing a problem in a system is understanding it, and the first step to that is observing the system's behaviour, especially the bits that fall outside desirable parameters. You hook some kind of monitor or debugger on to the system, gather data, analyse, from the results hopefully discover potential solutions. There's a broad spectrum of potential problems, from the system not performing optimally to it causing catastrophic failure across connected systems. The system in this case is a person. The unwanted behaviour is the stuff associated with mental health issues: things going on the in the subject's consciousness that lead to the spectrum from unhappiness, through to self-harm and/or harm to others. But how do you monitor such behaviour?

Nowadays a lot of people interact with the Web through social networking sites. They pump out a lot of text data. So imagine a hook into that data that applies a bit of text analysis. A Facebook app, a twitter-subscriber, a blog feed aggregator etc. (ideally all of these). The lifestream stuff.

Using myself as a sample subject, I have periods of mild depression (usually expressed as lethargy, lack of motivation, fortunately not much of the Dark Thoughts stuff). Also periods of heavy drinking, which sometimes lead to a bit of mania (lack of sleep being a big factor). If you tracked the text I output to the social networks there are plenty of markers: the depressed bits would be associated with lower output for starters. As well as incoherence during the boozy spells, I also seriously ramp up on sweariness and general antagonism.

It's easy to see how you could formulate a few chart plots from specific factors, like keywords for sweariness. But we have smarter ways to look at text, analyse the stuff across different dimensions. Initially I imagine it would make sense to obtain some baselines, corresponding to societal norms (Big Data!). Then onto individual norms. These would only make sense alongside other metrics that corresponded to something like Maslow's hierarchy - physical well-being and further up things like how items ticked off the todo list today, state of the bank account. Ideally you'd also want to monitor various other environmental factors - a trigger for me losing it has often been travel (especially to the uk :) Care would need to be taken not to conflate deviation from societal norms with anti-social behaviour, a bit of unhappiness with abnormality.

So ultimately, assuming you've got the data and done smart analysis, how do you fix the problem? I can't see there being any magic bullets, but given such a setup like this you could at least monitor the effects of medication, therapy or lifestyle changes. General solution I guess being to keep tweaking the variables and keeping whatever causes a net improvement.