DETECTING BEHAVIORAL ANOMALY IN SOCIAL NETWORKS USING SYMBOLIC DYNAMIC FILTERING

This paper investigates the use of Symbolic Dynamic Filtering (SDF) algorithms in detecting anomalous behavior trends in social networks. Data is generated from an agent-based discrete choice model, which relies on a Markov Decision Process framework for stochastic simulation of decision-making in a social setting, where choices and decisions by individuals are influenced by social interactions. We show that such collective imitative behavior leads to rapid unstable fluctuations in the society, the flucituation statistics being a weak function of the number of extremist nodes present in the network as well as the prevailing political climate. In this paper, using a time-trace of global opinions in the said society, we investigate the effectiveness of SDF in estimating the number of extremist nodes in a network, and studying the role of unpopular government policies as an enabler of political instability. Spread of influence and 'recruiting' by extremist groups through social networks has become an important political issue in recent years. This study is a step in the direction of building tools to preempt and intervene such efforts.