Collective behaviour of social bots is encoded in their temporal Twitter activity

TL;DR

This study reveals distinct collective behavioural patterns in social bots versus humans on Twitter during the UK EU referendum, highlighting differences in correlation structures and collective states.

Contribution

It demonstrates that social bot populations exhibit unique collective properties and correlation patterns, aiding in social bot detection and understanding online manipulation.

Findings

Social bots and humans show different correlation and co-spiking statistics.

Populations of social bots exhibit collective properties similar to critical systems.

Weak pairwise correlations coexist with collective correlated states.

Abstract

Computational propaganda deploys social or political bots to try to shape, steer and manipulate online public discussions and influence decisions. Collective behaviour of populations of social bots has not been yet widely studied, though understanding of collective patterns arising from interactions between bots would aid social bot detection. Here we show that there are significant differences in collective behaviour 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.