HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media

TL;DR

HAT4RD introduces a hierarchical adversarial training approach for rumor detection on social media, enhancing robustness and generalization by adding adversarial perturbations at multiple levels and verifying effectiveness across multiple datasets.

Contribution

The paper proposes a novel hierarchical adversarial training method for rumor detection that improves robustness and generalization over existing models.

Findings

Outperforms state-of-the-art methods on three rumor datasets.

Enhances model robustness against adversarial attacks.

Achieves better generalization with flatter loss landscapes.

Abstract

With the development of social media, social communication has changed. While this facilitates people's communication and access to information, it also provides an ideal platform for spreading rumors. In normal or critical situations, rumors will affect people's judgment and even endanger social security. However, natural language is high-dimensional and sparse, and the same rumor may be expressed in hundreds of ways on social media. As such, the robustness and generalization of the current rumor detection model are put into question. We proposed a novel \textbf{h}ierarchical \textbf{a}dversarial \textbf{t}raining method for \textbf{r}umor \textbf{d}etection (HAT4RD) on social media. Specifically, HAT4RD is based on gradient ascent by adding adversarial perturbations to the embedding layers of post-level and event-level modules to deceive the detector. At the same time, the detector uses stochastic gradient descent to minimize the adversarial risk to learn a more robust model. In this way, the post-level and event-level sample spaces are enhanced, and we have verified the robustness of our model under a variety of adversarial attacks. Moreover, visual experiments indicate that the proposed model drifts into an area with a flat loss landscape, leading to better generalization. We evaluate our proposed method on three public rumors datasets from two commonly used social platforms (Twitter and Weibo). Experiment results demonstrate that our model achieves better results than state-of-the-art methods.