Learning to Detect Few-Shot-Few-Clue Misinformation

Abstract

A large volume of false textual information has been disseminating for a long time since the prevalence of social media. The potential negative influence of misinformation on the public is a growing concern. Therefore, it is strongly motivated to detect online misinformation as early as possible. Few-shot-few-clue learning applies in this misinformation detection task when the number of annotated statements is quite few (called few shots) and the corresponding evidence is also quite limited in each shot (called few clues). Within the few-shot-few-clue framework, we propose a task-aware Bayesian meta-learning algorithm to extract shared patterns among different topics (i.e. different tasks) of misinformation. Moreover, we derive a scalable method, i.e., amortized variational inference, to optimize the Bayesian meta-learning algorithm. Empirical results on three benchmark datasets demonstrate the superiority of our algorithm. This work focuses more on optimizing parameters than deigning detection models, and will generate fresh insights into data efficient detection of online misinformation at early stages.