  The proliferation of fake news poses a serious threat to society, as it can
misinform and manipulate the public, erode trust in institutions, and undermine
democratic processes. To address this issue, we present FakeSwarm, a fake news
identification system that leverages the swarming characteristics of fake news.
To extract the swarm behavior, we propose a novel concept of fake news swarming
characteristics and design three types of swarm features, including principal
component analysis, metric representation, and position encoding. We evaluate
our system on a public dataset and demonstrate the effectiveness of
incorporating swarm features in fake news identification, achieving an f1-score
and accuracy of over 97% by combining all three types of swarm features.
Furthermore, we design an online learning pipeline based on the hypothesis of
the temporal distribution pattern of fake news emergence, validated on a topic
with early emerging fake news and a shortage of text samples, showing that
swarm features can significantly improve recall rates in such cases. Our work
provides a new perspective and approach to fake news detection and highlights
the importance of considering swarming characteristics in detecting fake news.
