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Correlated or synchronized bots commonly exist in social media sites such as Twitter. Bots work towards gaining human followers, participating in campaigns, and engaging in unethical activities such as spamming and false click generation. In this paper, we perform temporal pattern mining on bot activities in Twitter. We discover motifs (repeating behavior), discords (anomalous behavior), joins, bursts and dynamic clusters in activities of Twitter bots, and explain the significance of these temporal patterns in gaining competitive advantage over humans. Our analysis identifies a small set of indicators that separates bots from humans with high precision.
Chavoshi et al. (Sun,) studied this question.