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基于注意力图的谣言检测对抗式双对比学习

Bing Zhang¹, Tao Liu², Zunwang Ke³

¹School of Information Science and Engineering, Xinjiang University, Urumqi, China.

|April 22, 2024

概括

由于社交媒体的操纵,检测在线谣言具有挑战性. 我们的新型注意力图对抗双对比学习 (AGAD) 模型有效地区分谣言和可信信息,提高在线安全.

科学领域:

计算机科学计算机科学
社交媒体分析社交媒体分析
人工智能的人工智能

背景情况:

社交媒体是主要的新闻来源,增加了对错误信息和恶意操纵的脆弱性.
传统的谣言检测模型与被破坏的评论部分结构和复杂的操纵策略作斗争.
谣言的传播对公共卫生和金融稳定构成风险.

研究的目的:

开发一种新的谣言检测架构,能够处理复杂的社交媒体环境.
提高在线讨论中识别错误信息的准确性和稳定性.
减轻谣言对公共卫生和金融市场的负面影响.

主要方法:

提出了一种结合双重比较学习,对抗训练和注意力过器的新型架构.
引入了一个注意力过器模块来完善图形注意力网络 (GAT) 的评论数据.
实施了对抗性培训模块 (ADV) 以防范恶意评论的稳定性,以及双对比学习 (DCL) 组件以区分评论类型.

主要成果:

在谣言检测任务中,与最先进的算法相比,开发的AGAD模型表现出更高的性能.
实验结果证实了联合方法在识别和过错误信息方面的有效性.
该模型成功地增强了GAT网络处理的结构信息.

结论:

AGAD模型在谣言检测方面取得了重大进展,特别是在具有挑战性的社交媒体环境中.
整合注意力过器,对抗训练和双对比学习提供了强大的防御在线错误信息.
这项研究有助于创建一个更可靠的在线信息生态系统.

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