Dissecting a Social Botnet: Growth, Content and Influence in Twitter

TL;DR

This study analyzes a Syrian social botnet on Twitter over 35 weeks, examining its growth, content, and influence, revealing unique behaviors that differ from typical botnet patterns and regular users.

Contribution

It provides a detailed qualitative analysis of a specific social botnet, highlighting its distinct growth and content characteristics compared to common perceptions and regular users.

Findings

The botnet's growth and behavior did not align with typical botnet models.

The content of the botnet's tweets showed unique patterns distinguishing it from regular users.

The botnet influenced relevant discussions in ways that differ from standard bot activity.

Abstract

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.