  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.}
