Association Rule Analysis of the Repression-Dissent Dynamics

While civil violence is almost always countered by state violence, the opposite is not true. It is unclear whether state violence triggers or contains civil violence. Will those who have suffered from state repression pick up a weapon in anger or quiet down in fear and despair? A major stumbling block in the effort to tackle this problem of repression-dissent dynamics has been methodological. On one hand, small-N case studies, while good at revealing exact processes of interactions between state agents and social actors, cannot generalize the results beyond these cases. On the other, typical quantitative analyses of annual (monthly or weekly) event counts, while good at demonstrating general patterns, fail to detect interactive dynamics, as contentious interactions often happen in rapid sequence, sometimes within a matter of seconds, minutes, hours, and days.

   This paper attempts to overcome such a methodological deadlock by using a data set of popular contention in Great Britain (BRIT) collected by Charles Tilly. Unlike most event data sets, BRIT records the information about detailed sequences of contentious interactions within each event. This provides scholars with an unprecedented opportunity to examine contentious dynamics quantitatively. This paper applies a new method, “association rule analysis,” developed in the field of text mining of big data, to examine the repression-dissent dynamics. The method enables researchers to detect “rules of associations” or hidden patterns of contentious sequences in the form of probability statement (i.e. “the probability of police beating followed by students’ rioting is 42 %”). This paper uncovers, among others, state agents who are more likely to trigger civil violence, social actors who are more likely to resort to violence after state repression, and repression strategies which tend to evoke strong civil resistance. The association rule analysis will advance theories and methodologies concerning the dynamics of social movements.