融合外部知识和用户交互特征的虚假新闻检测

Identifying Fake News with External Knowledge and User Interaction Features

【目的】针对虚假新闻在社交媒体中肆意传播这一现象,通过融入外部知识特征和用户交互特征,构建多维度数据分类模型以提高虚假新闻检测的效率和准确性。【方法】提取虚假新闻文本的背景知识,通过维基知识图谱引入外部知识检测新闻内容与既有知识体系的内在一致性,同时根据心理学中相似效应理论分析传播链上的用户交互,通过改进图卷积网络的连接边权更真实地体现用户间相互影响,构建了一个融合外部知识、新闻内容、传播链特征与用户交互关系的多维度数据虚假新闻检测模型。【结果】在两个公开数据集Twitter15、Twitter16上验证模型的性能,与5个类似模型进行对比分析,该模型的准确率分别达到0.901和0.927。【局限】未考虑新闻附加内容中隐藏的知识信息和语言表达等其他特征,模型的可解释性也需要进一步提高。【结论】外部知识和传播链用户交互特征等多维度数据信息融合的检测模型能够有效提高虚假新闻的识别准确率。

[Objective]This paper proposes a multidimensional-data classification model to improve the efficiency of fake news detection.The new model incorporates external knowledge features and user interaction features to reduce fake news spreading in social media.[Methods]First,we extracted the background knowledge of fake news.Then,we introduced external knowledge through the Wikipedia knowledge graph to detect the consistency between the news content and the existing knowledge system.Third,we analyzed the user interaction on the communication chain according to the psychological“similarity effect”.Finally,we improved the connection edge weight of the graph convolutional network to reflect the interaction between users.[Results]We examined the new model’s performance with two public datasets,Twitter15 and Twitter16.Compared with the other five similar models,our model’s accuracy reached 0.901 and 0.927.[Limitations]We did not consider features like knowledge information and language expression hidden in the additional news content.The model’s interpretability needs to be further improved.[Conclusions]By integrating news content,external knowledge,and user interaction characteristics of the communication chain,the proposed model can effectively detect fake news.