  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.
