Towards predictive analytics in mental health care

Influence maximization, i.e., the problem of finding a small subset of nodes in a social network which can maximize the propagation of influence, has the potential to become a vital asset to identify and predict mental health related issues such as, predicting suicide, bulling, and radicalization. For example, predictive analytics in mental health can enable analyzing and exploring the factors involved in influencing people to participate in extremist activities. To address this challenge, in this paper, we analyze the influence maximization in mental health from effectiveness, efficiency, and scalability viewpoints. We present a social data analytics pipeline to enable analysts to engage with social data to explore the potential online radicalization. According to the predictive analytics, a particle swarm optimization influence maximization algorithm is proposed to facilitate selecting potential influential nodes. A context analytics algorithm is proposed to analyze the social data and the user activity patterns to learn how influence flows in social networks. We conducted intensive experiments based on real dataset and illustrate the effectiveness and efficiency of the proposed algorithms.