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Article detail · 2020

Scalable and Generalizable Social Bot Detection through Data Selection

Journal

Proceedings of the AAAI Conference on Artificial Intelligence
OpenAlex Open access · diamond Citations 355 Top 1% Percentile 100.0% FWCI 122.91
Year
2020
Type
conference-paper

Data source split

  • YÖKSİS venue Proceedings of the AAAI Conference on Artificial Intelligence
  • OpenAlex OpenAlex enrichment (abstract, citations, topics)

Abstract

OpenAlex · English

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

355 citations

OpenAlex cited_by_count (cache / database)

Authors

No author information.