Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection

Abstract

User-generated content (e.g., tweets and profile descriptions) and shared content between users (e.g., news articles) reflect a user's online identity. This paper investigates whether correlations between user-generated and user-shared content can be leveraged for detecting disinformation in online news articles. We develop a multimodal learning algorithm for disinformation detection. The latent representations of news articles and user-generated content allow that during training the model is guided by the profile of users who prefer content similar to the news article that is evaluated, and this effect is reinforced if that content is shared among different users. By only leveraging user information during model optimization, the model does not rely on user profiling when predicting an article's veracity. The algorithm is successfully applied to three widely used neural classifiers, and results are obtained on different datasets. Visualization techniques show that the proposed model learns feature representations of unseen news articles that better discriminate between fake and real news texts.

Bootstrapping Multi-View Representations for Fake News Detection

“…Manually removing false news one by one is laborious and expensive. Automatic Fake News Detection (FND) has become a hot research topic (Allein, Moens, and Perrotta 2021;Shu et al 2020a;Xue et al 2021). The FND techniques can effectively help analyze the probability of misconducting information.…”

“…Many existing multimodal FND schemes use the textual and visual features as integrated representations (Khattar et al 2019;Singhal et al 2020;Chen et al 2019;Allein, Moens, and Perrotta 2021). Nevertheless, the disentanglement of features from different views has not been thoroughly investigated.…”