Dynamical Evolution of Anti-social Phenomena: A Data Science Approach

Abstract

Human interactions can be either positive or negative, giving rise to different complex social or anti-social phenomena. The dynamics of these interactions often lead to certain spatio-temporal patterns and complex networks, which can be interesting to a wide range of researchers—from social scientists to data scientists. Here, we use the publicly available data for a range of anti-social and political events like ethnic conflicts, human right violations and terrorist attacks across the globe. We aggregate these anti-social events over time, and study the temporal evolution of these events. We present here the results of several time-series analyses like recurrence intervals, Hurst R/S analysis, etc., that reveal the long memory of these time-series. Further, we filter the data country-wise, and study the time-series of these anti-social events within the individual countries. We find that the time-series of these events have interesting statistical regularities and correlations. Using multi-dimensional scaling technique, the countries are then grouped together in terms of the co-movements with respect to temporal growths of these anti-social events. The data science approaches to studying these anti-social phenomena may provide a deeper understanding about their formations and spreading. The results can help in framing public policies and creating strategies that can check their spread and inhibit these anti-social phenomena.