Synthesis of Affective Expressions and Artificial Intelligence to Discover Mental Distress in Online Community.

Looking at the rapidity the social media has gained ascendancy in the society, coupled with considerable shortage of addressing the health of the social media users, there is a pressing need for employing mechanized systems to help identify individuals at risk. In this study, we investigated potential of people's social media language in order to predict their vulnerability towards the future episode of mental distress. This work aims to (a) explore the most frequent affective expressions used by online users which reflect their mental health condition and (b) develop predictive models to detect users with risk of psychological distress. In this paper, dominant sentiment extraction techniques were employed to quantify the affective expressions and classify and predict the incident of psychological distress. We trained a set of seven supervised machine learning classifiers on logs crowd-sourced from 2500 Indian Social Networking Sites (SNS) users and validated with 3149 tweets collected from Indian Twitter. We test the model on these two different SNS datasets with different scales and ground truth labeling method and discuss the relationship between key factors and mental health. Performance of classifiers is evaluated at all classification thresholds; accuracy, precision, recall, F1-score. and experimental results show a better traction of accuracies ranging from ~ 82 to ~ 99% as compared to the models of relevant existing studies. Thus, this paper presents a mechanized decision support system to detect users' susceptibility towards mental distress and provides several evidences that it can be utilized as an efficient tool to preserve the psychological health of the social media users.