Dissecting a Social Botnet: Growth, Content and Influence in Twitter
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Anatomy of an AI-powered malicious social botnet
Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention,…
AnatomyDISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification
The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the idea of the "echo chamber." However, the …
MisinformationTopic ClassificationBeyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents
Driven by large language models (LLMs), social bot can autonomously engage in local interactions, whose human-like behaviors enable them to evade social bot detection. However, while these botnets exhibit realistic local…
Graph Neural NetworkIoT Botnet Detection Using an Economic Deep Learning Model
The rapid progress in technology innovation usage and distribution has increased in the last decade. The rapid growth of the Internet of Things (IoT) systems worldwide has increased network security challenges created by…
Deep LearningIntrusion DetectionA Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privac…
Intrusion DetectionFederated Learning