College of Management, Nanjing University of Posts and Telecommunications, Nanjing 210000, China.
College of Foreign Languages, Nanjing University of Posts and Telecommunications, Nanjing 210000, China.
Comput Intell Neurosci. 2022 Mar 30;2022:5738404. doi: 10.1155/2022/5738404. eCollection 2022.
Rapid development of the Internet has contributed to the widespread adoption of social network platforms. Network media plays an important role in the process of public opinion dissemination and bears significant social responsibility. Public opinion mining is of great significance for online media to improve the quality of content provision and enhance media credibility. How to make full use of user-generated content is the key to improving the accuracy of position detection tasks. In this paper, we proposed a stance detection model based on user feature fusion by using comments of netizens in false news events on Weibo as research content. The method of feature fusion is adopted to integrate vectors including user sentiment, cognitive features, and text feature at the feature layer for model training and position prediction. The model is evaluated on a dataset of related microblog comments in false news. The result shows that our proposed method has a certain improvement in the effect of stance detection.
互联网的飞速发展促进了社交网络平台的广泛采用。网络媒体在舆论传播过程中发挥着重要作用，承担着重大的社会责任。舆情挖掘对于提高在线媒体内容提供质量和增强媒体可信度具有重要意义。如何充分利用用户生成的内容是提高立场检测任务准确性的关键。本文以微博中虚假新闻事件的网民评论作为研究内容，提出了一种基于用户特征融合的立场检测模型。该方法采用特征融合的方法，在特征层上融合包括用户情感、认知特征和文本特征在内的向量，用于模型训练和立场预测。在虚假新闻相关微博评论的数据集上对模型进行评估。结果表明，所提出的方法在立场检测效果上有一定的提高。
