文章基本信息

标题：Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis
本地全文：下载
作者：Panagiotis Kasnesis ; Lazaros Toumanidis ; Charalampos Z. Patrikakis 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：10
页码：409
DOI：10.3390/info12100409
语种：English
出版社：MDPI Publishing
摘要：The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post’s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results.