Abstract:

The accurate recognition of fake news is an important research topic in the online environment, where distinguishing information explosion and authenticity is difficult. Existing studies mostly use multiple deep learning models to extract multivariate semantic features to capture different levels of semantic information in the text; however, the simple splicing of these features causes information redundancy and noise, limiting detection accuracy and generalization, and effective deep fusion methods are not available. In addition, existing studies tend to ignore the impact of dual sentiments co-constructed by news content and its corresponding comments on revealing news authenticity. This paper proposes a Dual Emotion and Multi-feature Fusion based Fake News Detection (DEMF-FND) model to address these problems. First, the emotional features of news and comments are extracted by emotion analysis. The emotional difference features reflecting the correlation between the two are introduced using similarity computation, and a dual emotion feature set is constructed. Subsequently, a fusion mechanism based on multihead attention is used to deeply fuse the global and local semantic features of the news text captured by a Bidirectional Long Short-Term Memory (BiLSTM) network with a designed Integrated Static-Dynamic Embedded Convolutional Neural Network (ISDE-CNN). Eventually, the dual emotion feature set is concatenated with the semantic features obtained by deep fusion and fed into a classification layer consisting of a fully connected layer, to determine news authenticity. Experimental results show that the proposed method outperforms the baseline method in terms of benchmark metrics on three real datasets, namely Weibo20, Twitter15, and Twitter16, and achieves 2.5, 2.3, and 5.5 percentage points improvements in accuracy, respectively, highlighting the importance of dual emotion and the deep fusion of semantic features in enhancing the performance of fake news detection.

Key words: social media, fake news detection, deep learning, emotion analysis, feature fusion

摘要：

在信息爆炸且真伪难辨的网络环境中，精准识别虚假新闻成为一项重要的研究课题。现有研究多采用多种深度学习模型提取多元语义特征，以捕捉文本中不同层次的语义信息，但简单拼接这些特征会导致信息冗余和噪声，限制检测的准确性和泛化性，目前缺乏有效的深度融合方法。此外，现有研究往往忽视了新闻内容与其对应评论共同构建的双重情感对揭示新闻真实性的影响。针对上述问题，提出一种基于双重情感和多特征融合的虚假新闻检测（DEMF-FND）模型。首先，通过情感分析提取新闻和评论的情感特征，并利用相似度计算引入反映两者关联性的情感差异特征，构建双重情感特征集。然后，采用基于多头注意力的融合机制，将双向长短期记忆网络（BiLSTM）与设计的集成静态-动态嵌入的卷积神经网络（ISDE-CNN）所捕捉的新闻文本全局与局部语义特征进行深度融合。最终，将双重情感特征集与经深度融合得到的语义特征拼接融合，输入由全连接层构成的分类层，以判断新闻的真假。实验结果显示，该方法在Weibo20、Twitter15和Twitter16 3个真实数据集上的基准指标均优于基线方法，在准确率上分别实现了2.5、2.3和5.5百分点的提升，凸显了双重情感和深度融合语义特征在提升虚假新闻检测性能方面的重要性。

关键词: 社交媒体, 虚假新闻检测, 深度学习, 情感分析, 特征融合