基于特征间和特征内注意力网络的可解释谣言检测
Explainable Rumor Detection using Inter and Intra-feature Attention Networks
摘要：
随着社交媒体变得无处不在，来自该媒体的信息消费也增加了。然而，随着这种增长而出现的一个严重问题是谣言的传播。因此，谣言识别是一项非常关键的任务，对经济、民主以及公共健康和安全都有重要意义。在本文中，我们通过设计一个模块化的可解释架构来解决社交媒体中谣言的自动检测问题，该架构使用潜在和手工制作的功能，并且可以根据需要扩展到许多新的功能类别。这种方法将允许最终用户不仅确定社交媒体上的信息是否是真实的谣言，而且还可以解释算法得出结论的原因。使用注意力机制，我们能够解释每个特征的相对重要性以及特征类本身的相对重要性。这种方法的优点是，架构可以扩展到更多的手工制作的功能，因为它们变得可用，也进行广泛的测试，以确定这些功能在最终决策的相对影响。在流行的数据集上进行的广泛实验和对11种当代算法的基准测试表明，我们的方法在F分数和准确性方面表现得更好，同时也是可解释的。
英文摘要：
With social media becoming ubiquitous, information consumption from this media has also increased. However, one of the serious problems that have emerged with this increase, is the propagation of rumors. Therefore, rumor identification is a very critical task with significant implications to economy, democracy as well as public health and safety. We tackle the problem of automated detection of rumors in social media in this paper by designing a modular explainable architecture that uses both latent and handcrafted features and can be expanded to as many new classes of features as desired. This approach will allow the end user to not only determine whether the piece of information on the social media is real of a rumor, but also give explanations on why the algorithm arrived at its conclusion. Using attention mechanisms, we are able to interpret the relative importance of each of these features as well as the relative importance of the feature classes themselves. The advantage of this approach is that the architecture is expandable to more handcrafted features as they become available and also to conduct extensive testing to determine the relative influences of these features in the final decision. Extensive experimentation on popular datasets and benchmarking against eleven contemporary algorithms, show that our approach performs significantly better in terms of F-score and accuracy while also being interpretable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
