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In light of the growing impact of disinformation on social, economic, andpolitical landscapes, accurate and efficient identification methods areincreasingly critical. This paper introduces HyperGraphDis, a novel approachfor detecting disinformation on Twitter that employs a hypergraph-basedrepresentation to capture (i) the intricate social structures arising fromretweet cascades, (ii) relational features among users, and (iii) semantic andtopical nuances. Evaluated on four Twitter datasets -- focusing on the 2016U.S. Presidential election and the COVID-19 pandemic -- HyperGraphDisoutperforms existing methods in both accuracy and computational efficiency,underscoring its effectiveness and scalability for tackling the challengesposed by disinformation dissemination. HyperGraphDis displays exceptionalperformance on a COVID-19-related dataset, achieving an impressive F1 score(weighted) of approximately 89.5%. This result represents a notable improvementof around 6% compared to existing methods. Additionally, significantenhancements in computation time are observed for both model training andinference. In terms of model training, completion times are accelerated by afactor ranging from 2.3 to 9.3 compared to previous methods. Similarly, duringinference, computation times are 1.3 to 7.2 times faster than thestate-of-the-art.

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