Using Twitter Content to Predict Psychopathy

Abstract

An ever-growing number of users share their thoughts and experiences using the Twitter micro logging service. Although sometimes dismissed as containing too little content to convey significant information, these messages can be combined to build a larger picture of the user posting them. One particularly notable personality trait which can be discovered this way is psychopathy: the tendency for disregarding others and the rule of society. In this paper, we explore techniques to apply data mining towards the goal of identifying those who score in the top 1.4% of a well-known psychopathy metric using information available from their Twitter accounts. We apply a newly-proposed form of ensemble learning, Select RUSBoost (which adds feature selection to our earlier imbalance-aware ensemble in order to resolve high dimensionality), employ four classification learners, and use four feature selection techniques. The results show that when using the optimal choices of techniques, we are able to achieve an AUC value of 0.736. Furthermore, these results were only achieved when using the Select RUSBoost technique, demonstrating the importance of feature selection, data sampling, and ensemble learning. Overall, we show that data mining can be a valuable tool for law enforcement and others interested in identifying abnormal psychiatric states from Twitter data.