Dissecting a Social Botnet: Growth, Content and Influence in Twitter
This study provides insights into social botnet dynamics for researchers and policymakers, but it is incremental as it focuses on a single case without broad generalizations.
The paper analyzed a specific social botnet on Twitter over 35 weeks to understand its growth, content differences from regular users, and influence on discussions, finding that its behavior did not align with common conceptions of botnets and identifying distinguishing aspects.
Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users.