From single-task to multi-task: Unveiling the dynamics of knowledge transfers in disinformation detection

The spread of misinformation and fake news on digital platforms poses significant societal challenges, underscoring the need for robust detection. Multi-task learning leverages relationships among disinformation-related tasks (e.g., stance detection, rumor classification) to enhance detection; however, it risks negative transfer, potentially degrading performance instead of achieving positive transfer. In this paper, we systematically investigate the mechanisms underlying positive and negative transfers across a comprehensive set of disinformation-related tasks, including Sentiment Analysis (SA), Fake News Detection (FND), Stance Detection (SD), and Topic Detection (TD). Specifically, we pioneer the use of explanations to uncover the differences between models trained under single-task and multi-task settings. Our results reveal instances of positive transfer across several task combinations, with multi-task learning yielding performance improvements of 3.26%, 6.57%, and 0.62% for SA, FND and TD tasks, respectively. Furthermore, when comparing explanations of single-task and multi-task models, we find that positive transfer refines the knowledge that can already be learnt in single-task settings by incorporating additional patterns from other tasks. Conversely, negative transfer significantly undermines models' knowledge to the extent that their explanations are equivalent to a random perturbation of the explanations generated by their single-task counterparts.