HyperGraphDis: Leveraging Hypergraphs for Contextual and Social-Based Disinformation Detection
CoRR(2023)
摘要
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
of around 6
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.
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关键词
disinformation,hypergraphs,social-based
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