Evaluating Public Anxiety for Topic-Based Communities in Social Networks

Although individual anxiety evaluation has been well studied, there is still not much work on evaluating public anxiety of groups, especially in the form of communities on social networks, which can be leveraged to detect mental healthness of a society. However, we cannot simply average individual anxiety scores to evaluate a community's public anxiety, because following factors should be considered: (1) impacts from interpersonal relations on each individual group member's anxiety levels (the ${\tt Structural}$ component); (2) topic-based discussions which reflect a community's anxiety status (the ${\tt Topical}$ component). In this paper, we initiate the study of evaluating public anxiety of Topic-based Social Network Communities ( $\textsc {TSNC}$ ). We propose an evaluation framework to project the anxiety level of a $\textsc {TSNC}$ into a score in the [0,1] range. We devise a cascading model to dynamically compute the individual anxiety scores using the ${\tt Structural}$ influence. We design a probabilistic model to measure anxiety score of social network messages using a generalized user, and compose a tree structure ( ${\tt MC}$ - ${\tt Tree}$ ) to effectively compute the anxiety score of a $\textsc {TSNC}$ from the ${\tt Topical}$ aspect. For large communities, to avoid expensive real-time computing, we use a small sample to compute the public anxiety within given confidence interval. The effectiveness of our model are verified by precision and recall in an empirical study on real-world Weibo and Twitter data sets.