An Event Correlation Filtering Method for Fake News Detection

by   Hao Li, et al.

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.


page 1

page 2

page 3

page 4


Weak Supervision for Fake News Detection via Reinforcement Learning

Today social media has become the primary source for news. Via social me...

LTCR: Long-Text Chinese Rumor Detection Dataset

False information can spread quickly on social media, negatively influen...

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

Fake news travels at unprecedented speeds, reaches global audiences and ...

Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection

Fake news spreads at an unprecedented speed, reaches global audiences an...

Universal Fake News Collection System using Debunking Tweets

Large numbers of people use Social Networking Services (SNS) for easy ac...

Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach

We present ProxiModel, a novel event mining framework for extracting hig...

Deception Detection with Feature-Augmentation by soft Domain Transfer

In this era of information explosion, deceivers use different domains or...

Please sign up or login with your details

Forgot password? Click here to reset