LLM-Driven Adversarial Example Synthesis for Emerging Topic Rumor Detection on Social Media

Rumor detection is essential for building a responsible web and internet ecosystem, which has attracted significant attention from the research community. However, emerging topic rumor detection, i.e., identify rumors at the early stages of a topic's emergence where only limited discussions can be observed, still remains a challenge. Technically, this scenario is accompanied by the issues of data scarcity on emerging topics and the data distribution discrepancy between old topics and emerging new topic. In this paper, we propose a new framework termed LLM-driven ADversarial Example Synthesis (LADES) for emerging topic rumor detection. LADES utilizes Large Language Models (LLMs) for generating readable and contextually coherent adversarial examples. The generated adversarial examples not only expand the training set to tackle the data scarcity issue, but also act as a bridge to connect the data distribution of old and new topics. To overcome training instability in adversarial example generation, LADES introduces a gradient-free Markov Chain Monte Carlo (MCMC) sampling method. This method ensures adversarial examples are readable and contextually coherent by harnessing LLMs, while promoting effective attacks through entropy-based sampling that targets model uncertainty. To mitigate the impact of potential mislabeling in synthetic data, LADES implements a meta-mixed-learning mechanism. This mechanism dynamically adjusts the weights of synthetic adversarial examples, guided by limited labeled data from emerging topics, thereby alleviating the data noise.