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The rapid and accurate identification of bot accounts in online socialnetworks is an ongoing challenge. In this paper, we propose BOTTRINET, aunified embedding framework that leverages the textual content posted byaccounts to detect bots. Our approach is based on the premise that accountpersonalities and habits can be revealed through their contextual content. Toachieve this, we designed a triplet network that refines raw embeddings usingmetric learning techniques. The BOTTRINET framework produces word, sentence,and account embeddings, which we evaluate on a real-world dataset, CRESCI2017,consisting of three bot account categories and five bot sample sets. Ourapproach achieves state-of-the-art performance on two content-intensive botsets, with an average accuracy of 98.34% and f1score of 97.99%. Moreover, ourmethod makes a significant breakthrough on four content-less bot sets, with anaverage accuracy improvement of 11.52% and an average f1score increase of16.70%. Our contribution is twofold: First, we propose a unified and effectiveframework that combines various embeddings for bot detection. Second, wedemonstrate that metric learning techniques can be applied in this context torefine raw embeddings and improve classification performance. Our approachoutperforms prior works and sets a new standard for bot detection in socialnetworks.

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