Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
This addresses disinformation detection for online platforms, but it is incremental as it builds on existing neural classifiers with a novel multimodal approach.
The paper tackled the problem of detecting disinformation in online news articles by leveraging correlations between user-generated content and shared articles, resulting in improved feature representations that better discriminate between fake and real news texts across multiple datasets.
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.