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

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URI: https://hdl.handle.net/20.500.12761/1803
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Autor(es)
Salamanos, Nikos; Leonidou, Pantelitsa; Laoutaris, Nikolaos; Sirivianos, Michael; Aspri, Maria; Paraschiv, Marius
Fecha
2024-06-03
Resumen
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 6% compared to existing 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 9.3 compared to previous methods. Similarly, during inference, computation times are 1.3 to 7.2 times faster than the state-of-the-art.
Compartir
Ficheros
main article (675.5Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1803
Metadatos
Mostrar el registro completo del ítem

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