SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection

TL;DR

SATAR is a self-supervised framework that learns comprehensive Twitter user representations, improving bot detection accuracy, generalizability, and adaptability to evolving bot behaviors across diverse datasets.

Contribution

Introduces SATAR, a self-supervised learning method that leverages semantics, properties, and neighborhood info for robust Twitter user representation and bot detection.

Findings

Outperforms existing methods on various datasets.

Generalizes well to real-world scenarios.

Adapts effectively to evolving Twitter bots.

Abstract

Twitter has become a major social media platform since its launching in 2006, while complaints about bot accounts have increased recently. Although extensive research efforts have been made, the state-of-the-art bot detection methods fall short of generalizability and adaptability. Specifically, previous bot detectors leverage only a small fraction of user information and are often trained on datasets that only cover few types of bots. As a result, they fail to generalize to real-world scenarios on the Twittersphere where different types of bots co-exist. Additionally, bots in Twitter are constantly evolving to evade detection. Previous efforts, although effective once in their context, fail to adapt to new generations of Twitter bots. To address the two challenges of Twitter bot detection, we propose SATAR, a self-supervised representation learning framework of Twitter users, and apply it to the task of bot detection. In particular, SATAR generalizes by jointly leveraging the semantics, property and neighborhood information of a specific user. Meanwhile, SATAR adapts by pre-training on a massive number of self-supervised users and fine-tuning on detailed bot detection scenarios. Extensive experiments demonstrate that SATAR outperforms competitive baselines on different bot detection datasets of varying information completeness and collection time. SATAR is also proved to generalize in real-world scenarios and adapt to evolving generations of social media bots.