社交媒体时代给我们带来便利的同时也造成了谣言泛滥,因此通过人工智能技术进行谣言检测具有重要的研究价值。尽管基于深度学习的谣言检测取得了很好的效果,但其大多数是根据潜在特征进行谣言检测的,无法学习情感与语义之间的相关性,同时忽视了从情感角度提供解释。为解决上述问题,该文提出一种基于双重情感感知的可解释谣言检测模型,旨在利用协同注意力机制分别学习谣言语义与用户评论情感,以及谣言情感与用户评论情感的相关性进行谣言检测,并通过协同注意力权重从情感角度提供合理的解释。在公开的Twitter15、 Twitter16和Weibo20数据集上的实验结果表明,该文提出的模型与对比模型相比,在准确率上分别提高了3.9%,3.9%和4.4％,且具有合理的可解释性。
Abstract
The identification of rumors is of substantial significance research value. Current deep learning-based solution brings excellent results, but fails in capturing the relationship between emotion and semantics or providing emotional explanations. This paper proposes a dual emotion-aware method for interpretable rumor detection, aiming to provide a reasonable explanation from an emotional point of view via co-attention weights. Compared with contrast model, the accuracy is increased by 3.9%,3.3% and 4.4% on the public Twitter15, Twitter16, and Weibo20 datasets.
关键词
谣言检测 /
协同注意力 /
情感特征 /
可解释性 /
语义特征
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Key words
rumor detection /
co-attention /
emotion feature /
interpretable /
semantics feature
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参考文献
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脚注
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基金
国家社会科学基金(20BXW101,18XWW015);武警工程大学“优秀研究生培养计划”课题
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