HyperGraphDis: Leveraging Hypergraphs for Contextual and Social-Based Disinformation Detection


  • Nikos Salamanos Cyprus University of Technology
  • Pantelitsa Leonidou Cyprus University of Technology
  • Nikolaos Laoutaris IMDEA Networks Institute
  • Michael Sirivianos Cyprus University of Technology
  • Maria Aspri LSTECH ESPANA SL
  • Marius Paraschiv IMDEA Networks Institute




In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter datasets -- focusing on the 2016 U.S. presidential election and the COVID-19 pandemic -- HyperGraphDis outperforms existing methods in both accuracy and computational efficiency, underscoring its effectiveness and scalability for tackling the challenges posed by disinformation dissemination. HyperGraphDis displays exceptional performance on a COVID-19-related dataset, achieving an impressive F1 score (weighted) of approximately 89.5%. This result represents a notable improvement of around 4% compared to the other state-of-the-art methods. Additionally, significant enhancements in computation time are observed for both model training and inference. In terms of model training, completion times are accelerated by a factor ranging from 2.3 to 7.6 compared to the second-best method across the four datasets. Similarly, during inference, computation times are 1.3 to 6.8 times faster than the state-of-the-art.




How to Cite

Salamanos, N., Leonidou, P., Laoutaris, N., Sirivianos, M., Aspri, M., & Paraschiv, M. (2024). HyperGraphDis: Leveraging Hypergraphs for Contextual and Social-Based Disinformation Detection. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 1381-1394. https://doi.org/10.1609/icwsm.v18i1.31396