融合新闻传播模式和传播者情感偏好的虚假新闻检测研究

Fake News Detection with Dissemination Patterns and Sentiment Preferences

【目的】解决现有基于新闻传播模式的虚假新闻检测研究未能充分挖掘并融合传播者情感偏好特征的问题,提升虚假新闻检测模型准确率。【方法】构建一种融合新闻传播模式和传播者情感偏好的虚假新闻检测模型USPGCN。首先,从传播者历史发文中挖掘传播者情感偏好特征,并用新闻文本的情感特征丰富新闻文本特征;其次,以新闻传播模式为基础,通过图卷积神经网络以及混合池化函数,融合新闻传播者情感偏好和新闻传播模式;最后,将丰富后的新闻文本特征与池化函数的结果融合,输入到分类器中得到最终的分类结果。【结果】在公开的数据集GossipCop和PolitiFact上,将所提出模型与基线模型进行比较,该模型的精确率分别达到0.9739和0.9048,优于基线模型,证明了该模型的有效性。【局限】暂未考虑传播者跟风转发等特殊情况。【结论】融合新闻传播模式和传播者情感偏好的模型能够有效提高虚假新闻检测识别的准确率。

[Objective]The existing fake news detection models based on dissemination patterns could not sufficiently explore and integrate the users’sentiment preferences.This paper aims to address this issue and improve the accuracy of these models.[Methods]We constructed a fake news detection model,USPGCN,which integrated news dissemination mode and communicator emotional preferences.Firstly,we examined the emotional preference characteristics of communicators from their historical posts and enriched the news text features with their emotional characteristics.Secondly,based on the news dissemination patterns,we combined the communicator’s sentiment preferences and the news dissemination patterns using the graph convolutional neural network and mixed pooling functions.Thirdly,we integrated the enriched news text features and the pooling function results.Finally,we fed these data into a classifier to obtain the final classification results.[Results]Compared with baseline models on the publicly available datasets GossipCop and PolitiFact,the new model’s precision reached 0.9739 and 0.9048,respectively,outperforming the baseline models and demonstrating its effectiveness.[Limitations]This study does not consider some cases,such as communicators sharing news due to trends.[Conclusions]The method integrating news dissemination patterns and communicator sentiment preferences can effectively improve the accuracy of fake news detection.