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

arXiv: https://arxiv.org/abs/1604.03627 · HTML: https://mlsanity.com/paper/1604.03627/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2016.

## Built on by (most cited first, 7 of 7)

- [RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search](https://mlsanity.com/paper/2206.06757/) (2022)
- [FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection](https://mlsanity.com/paper/2303.07113/) (2023)
- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation](https://mlsanity.com/paper/2604.10678/) (2026)
- [Bots Don't Sit Still: A Longitudinal Study of Bot Behaviour Change, Temporal Drift, and Feature-Structure Evolution](https://mlsanity.com/paper/2512.17067/) (2025)
- [Emotion-Aware Clickbait Attack in Social Media](https://mlsanity.com/paper/2604.27369/) (2026)

## Abstract (arXiv)

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.