  Nowadays, fake news easily propagates through online social networks and
becomes a grand threat to individuals and society. Assessing the authenticity
of news is challenging due to its elaborately fabricated contents, making it
difficult to obtain large-scale annotations for fake news data. Due to such
data scarcity issues, detecting fake news tends to fail and overfit in the
supervised setting. Recently, graph neural networks (GNNs) have been adopted to
leverage the richer relational information among both labeled and unlabeled
instances. Despite their promising results, they are inherently focused on
pairwise relations between news, which can limit the expressive power for
capturing fake news that spreads in a group-level. For example, detecting fake
news can be more effective when we better understand relations between news
pieces shared among susceptible users. To address those issues, we propose to
leverage a hypergraph to represent group-wise interaction among news, while
focusing on important news relations with its dual-level attention mechanism.
Experiments based on two benchmark datasets show that our approach yields
remarkable performance and maintains the high performance even with a small
subset of labeled news data.
