Tensor Factorization with Label Information for Fake News Detection

The buzz over the so-called "fake news" has created concerns about a degenerated media environment and led to the need for technological solutions. As the detection of fake news is increasingly considered a technological problem, it has attracted considerable research. Most of these studies primarily focus on utilizing information extracted from textual news content. In contrast, we focus on detecting fake news solely based on structural information of social networks. We suggest that the underlying network connections of users that share fake news are discriminative enough to support the detection of fake news. Thereupon, we model each post as a network of friendship interactions and represent a collection of posts as a multidimensional tensor. Taking into account the available labeled data, we propose a tensor factorization method which associates the class labels of data samples with their latent representations. Specifically, we combine a classification error term with the standard factorization in a unified optimization process. Results on real-world datasets demonstrate that our proposed method is competitive against state-of-the-art methods by implementing an arguably simpler approach.Comment: Presented at the Workshop on Reducing Online Misinformation Exposure ROME 201

Fake News Detection Based on Subjective Opinions

Fake news fluctuates social media, leading to harmful consequences. Several types of information could be utilized to detect fake news, such as news content features and news propagation features. In this study, we focus on the user spreading news behaviors on social media platforms and aim to detect fake news more effectively with more accurate data reliability assessment. We introduce Subjective Opinions into reliability evaluation and proposed two new methods. Experiments on two popular real-world datasets, BuzzFeed and PolitiFact, validates that our proposed Subjective Opinions based method can detect fake news more accurately than all existing methods, and another proposed probability based method achieves state-of-art performance