Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity

Abstract

Computational propaganda deploys social or political bots to try to shape, steer, and manipulate online public discussions and influence decisions. Collective behavior of populations of social bots has not been yet widely studied, although understanding of collective patterns arising from interactions between bots would aid social bot detection. In this study, we show that there are significant differences in collective behavior between population of bots and population of humans as detected from their Twitter activity. Using a large dataset of tweets we have collected during the UK-EU referendum campaign, we separated users into population of bots and population of humans based on the length of sequences of their high-frequency tweeting activity. We show that, while pairwise correlations between users are weak, they co-exist with collective correlated states; however the statistics of correlations and co-spiking probability differ in both populations. Our results demonstrate that populations of social bots and human users in social media exhibit collective properties similar to the ones found in social and biological systems placed near a critical point.