BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning

Understand

The rapid and accurate identification of bot accounts in online social networks is an ongoing challenge.

• In this paper, we propose BOTTRINET, a unified embedding framework that leverages the textual content posted by accounts to detect bots.
• Our approach is based on the premise that account personalities and habits can be revealed through their contextual content.
• To achieve this, we designed a triplet network that refines raw embeddings using metric learning techniques.