Very interesting question @lizcrampton and in terms of predicting behaviour, one of my interests.
Have you considered adapting recommender systems and machine learning to predict user’s behaviour?
Recommender systems are used in a number of online industries, notably retail for point of sale product suggestion and video on demand for predicting the types of videos a viewer might want to watch next.
Many of the recommendations being made are based on rules input by humans behind the scenes, although some algorithms use machine learning to spot behaviours and base their recommendations, at least partly, on behaviours which are observed and analysed in real time.
With this in mind here’s a paper discussing Machine Classification and Analysis of Suicide-Related Communication on Twitter.
And an excerpt:
In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide.
And here’s another although dealing with the more general issue of threat analysis - but a useful backgrounder: Recommender Systems for Intelligence Analysts.
Hope the above is helpful.