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Nowadays, social network platforms have been the prime source for people toexperience news and events due to their capacities to spread informationrapidly, which inevitably provides a fertile ground for the dissemination offake news. Thus, it is significant to detect fake news otherwise it could causepublic misleading and panic. Existing deep learning models have achieved greatprogress to tackle the problem of fake news detection. However, training aneffective deep learning model usually requires a large amount of labeled news,while it is expensive and time-consuming to provide sufficient labeled news inactual applications. To improve the detection performance of fake news, we takeadvantage of the event correlations of news and propose an event correlationfiltering method (ECFM) for fake news detection, mainly consisting of the newscharacterizer, the pseudo label annotator, the event credibility updater, andthe news entropy selector. The news characterizer is responsible for extractingtextual features from news, which cooperates with the pseudo label annotator toassign pseudo labels for unlabeled news by fully exploiting the eventcorrelations of news. In addition, the event credibility updater employsadaptive Kalman filter to weaken the credibility fluctuations of events. Tofurther improve the detection performance, the news entropy selectorautomatically discovers high-quality samples from pseudo labeled news byquantifying their news entropy. Finally, ECFM is proposed to integrate them todetect fake news in an event correlation filtering manner. Extensiveexperiments prove that the explainable introduction of the event correlationsof news is beneficial to improve the detection performance of fake news.

An Event Correlation Filtering Method for Fake News Detection