ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (7): 1353-1365.doi: 10.7544/issn1000-1239.2021.20200979

所属专题: 2021虚假信息检测专题

• 信息处理 • 上一篇    下一篇



  1. 1(亚利桑那州立大学计算机科学与工程系 美国亚利桑那州 坦佩 85281);2(伊利诺伊理工大学计算机科学系 美国伊利诺伊州 芝加哥 60616);3(重庆大学大数据与软件学院 重庆 400044) (
  • 出版日期: 2021-07-01

Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

Amrita Bhattacharjee1, Shu Kai2, Gao Min3, Liu Huan1   

  1. 1(Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, USA 85281);2(Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA 60616);3(School of Big Data and Software Engineering, Chongqing University, Chongqing 400044)
  • Online: 2021-07-01

摘要: 随着互联网的迅速发展及网络社会媒体中用户的增加,通过社会媒体发布和传播信息的真实性和质量受到日益广泛的关注.目前大部分公众已习惯从社会媒体平台与互联网获取新闻,甚至是获取受到高度关注的话题(如新冠病毒感染症状)的信息.鉴于网络信息生态系统非常嘈杂,充斥着错误和虚假信息并经常受到恶意媒介的污染,从中识别真实的信息成为一项艰巨任务.对此,研究者们已开始致力于虚假信息检测和减缓虚假信息传播影响方面的工作.讨论了网络信息生态系统中的虚假信息问题,特别是随着新冠病毒大爆发而来的“信息疫情”.随后,简述了虚假信息检测方法,分析了减缓虚假信息影响的方法,并探讨了虚假信息研究中的固有挑战.最后从跨学科角度阐述了检测和减缓虚假信息影响的方法和未来研究展望.

关键词: 虚假信息, 缓解虚假信息, 虚假信息检测, 社会媒体, 跨学科方法

Abstract: With the rapid increase in access to the internet and the subsequent growth in the population of social media users, the quality of information posted, disseminated, and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and, in general, the internet for news and even information regarding highly concerning issues such as COVID-19 symptoms and treatments. Given that the online information ecosystem is extremely noisy, fraught with misinformation and disinformation, and often contaminated by malicious agents spreading propaganda, identifying genuine and good quality information from disinformation is a challenging task for humans. In this regard, there is a significant amount of ongoing research in the directions of disinformation detection and mitigation. In this survey, we discuss the online disinformation problem, focusing on the recent ″infodemic″ in the wake of the coronavirus pandemic. We then proceed to discuss the inherent challenges in disinformation research, including data collection, early detection and effective mitigation, fact-checking based approaches, multi-modality approaches, and policy issues and fairness, and elaborate on the interdisciplinary approaches towards the detection and mitigation of disinformation, after a short overview of the various directions explored in computational detection and mitigation efforts.

Key words: disinformation, disinformation mitigation, disinformation detection, social media, inter-disciplinary approaches