Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan.
Maritime College, Guangdong Ocean University, Zhanjiang 524000, China.
Sensors (Basel). 2022 Sep 2;22(17):6652. doi: 10.3390/s22176652.
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 can 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 in question. We proposed a novel ierarchical dversarial raining method for umor 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 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, thereby, leading to better generalization. We evaluate our proposed method on three public rumor datasets from two commonly used social platforms (Twitter and Weibo). Our experimental results demonstrate that our model achieved better results compared with the state-of-the-art methods.
随着社交媒体的发展，社会交流发生了变化。虽然这方便了人们的沟通和获取信息，但也为谣言的传播提供了理想的平台。在正常或危急情况下，谣言可能会影响人们的判断，甚至危及社会安全。然而，自然语言具有高维和稀疏的特点，同样的谣言在社交媒体上可能有数百种表达方式。因此，当前的谣言检测模型的鲁棒性和泛化能力受到质疑。我们提出了一种新的基于分层对抗训练的社交媒体谣言检测方法（HAT4RD）。具体来说，HAT4RD 基于梯度上升，通过在帖子级别和事件级别模块的嵌入层中添加对抗性扰动来欺骗检测器。同时，检测器使用随机梯度下降来最小化对抗风险，以学习更鲁棒的模型。这样，帖子级别和事件级别样本空间得到了增强，我们验证了我们的模型在多种对抗攻击下的鲁棒性。此外，可视化实验表明，所提出的模型漂移到具有平坦损失景观的区域，从而导致更好的泛化能力。我们在来自两个常用社交平台（Twitter 和 Weibo）的三个公共谣言数据集上评估了我们提出的方法。我们的实验结果表明，与最先进的方法相比，我们的模型取得了更好的结果。
