  Nowadays, social network platforms have been the prime source for people to
experience news and events due to their capacities to spread information
rapidly, which inevitably provides a fertile ground for the dissemination of
fake news. Thus, it is significant to detect fake news otherwise it could cause
public misleading and panic. Existing deep learning models have achieved great
progress to tackle the problem of fake news detection. However, training an
effective deep learning model usually requires a large amount of labeled news,
while it is expensive and time-consuming to provide sufficient labeled news in
actual applications. To improve the detection performance of fake news, we take
advantage of the event correlations of news and propose an event correlation
filtering method (ECFM) for fake news detection, mainly consisting of the news
characterizer, the pseudo label annotator, the event credibility updater, and
the news entropy selector. The news characterizer is responsible for extracting
textual features from news, which cooperates with the pseudo label annotator to
assign pseudo labels for unlabeled news by fully exploiting the event
correlations of news. In addition, the event credibility updater employs
adaptive Kalman filter to weaken the credibility fluctuations of events. To
further improve the detection performance, the news entropy selector
automatically discovers high-quality samples from pseudo labeled news by
quantifying their news entropy. Finally, ECFM is proposed to integrate them to
detect fake news in an event correlation filtering manner. Extensive
experiments prove that the explainable introduction of the event correlations
of news is beneficial to improve the detection performance of fake news.
