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Fake news spreads at an unprecedented speed, reaches global audiences andposes huge risks to users and communities. Most existing fake news detectionalgorithms focus on building supervised training models on a large amount ofmanually labeled data, which is expensive to acquire or often unavailable. Inthis work, we propose a novel label noise-resistant mean teaching approach(LNMT) for weakly supervised fake news detection. LNMT leverages unlabeled newsand feedback comments of users to enlarge the amount of training data andfacilitates model training by generating refined labels as weak supervision.Specifically, LNMT automatically assigns initial weak labels to unlabeledsamples based on semantic correlation and emotional association between newscontent and the comments. Moreover, in order to suppress the noises in weaklabels, LNMT establishes a mean teacher framework equipped with labelpropagation and label reliability estimation. The framework measures a weaklabel similarity matrix between the teacher and student networks, andpropagates different valuable weak label information to refine the weak labels.Meanwhile, it exploits the consistency between the output class likelihoodvectors of the two networks to evaluate the reliability of the weak labels andincorporates the reliability into model optimization to alleviate the negativeeffect of noisy weak labels. Extensive experiments show the superiorperformance of LNMT.

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