Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

被引:5
|
作者
Jamialahmadi, Sarina [1 ]
Sahebi, Iman [1 ]
Sabermahani, Mohammad Mahdi [2 ]
Shariatpanahi, Seyed Pooya [1 ]
Dadlani, Aresh [3 ,4 ]
Maham, Behrouz [3 ]
机构
[1] Univ Tehran, Coll Engn, Sch Elect & Comp Engn, Tehran 1439957131, Iran
[2] Amirkabir Univ Technol, Dept Comp Engn, Tehran 158754413, Iran
[3] Nazarbayev Univ, Dept Elect & Comp Engn, Nur Sultan 010000, Kazakhstan
[4] Univ Alberta, Fac Arts Interdisciplinary Studies, Edmonton, AB T6G 2E7, Canada
关键词
Fake news; Social networking (online); Information integrity; Internet; Knowledge engineering; Feature extraction; Fabrication; Rumor detection; rumor verification; rumor stance classification; misinformation; social networks; datasets; NEWS;
D O I
10.1109/ACCESS.2022.3216835
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The emergence of the Internet as a ubiquitous technology has facilitated the rapid evolution of social media as the leading virtual platform for communication, content sharing, and information dissemination. In spite of revolutionizing the way news is delivered to people, this technology has also brought along with itself inevitable demerits. One such drawback is the spread of rumors expedited by social media platforms, which may provoke doubt and fear. Therefore, it is essential to debunk rumors before their widespread use. Over the years, many studies have been conducted to develop effective rumor verification systems. One aspect of such studies focuses on rumor stance classification, which involves the task of utilizing user viewpoints regarding a rumorous post to better predict the veracity of a rumor. Relying on user stances in rumor verification has gained significant importance, for it has resulted in significant improvements in the model performance. In this paper, we conduct a comprehensive literature review of rumor stance classification in complex online social networks (OSNs). In particular, we present a thorough description of these approaches and compare their performances. Moreover, we introduce multiple datasets available for this purpose and highlight their limitations. Finally, challenges and future directions are discussed to stimulate further relevant research efforts.
引用
收藏
页码:113131 / 113148
页数:18
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