论文标题
在线信息生态系统中的虚假信息：检测，缓解和挑战
Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
论文作者
论文摘要
随着互联网访问的迅速增加以及在线社交媒体用户的随后增长，通过这些平台发布，传播和消费的信息的质量是人们日益关注的问题。公众很大一部分转向社交媒体平台，一般而言，互联网以获取新闻，甚至有关高度有关COVID-19症状等问题的信息。鉴于在线信息生态系统非常嘈杂，充满了错误的信息和虚假信息，并且经常受到恶意代理商传播宣传的污染，因此从虚假信息中识别出真正有质的质量信息是人类的挑战性任务。在这方面，在虚假信息检测和缓解方向上正在进行大量的研究。在这项调查中，我们讨论了在线虚假信息问题，重点是冠状病毒大流行后的最近的“流行病”。然后，我们开始讨论虚假信息研究中的固有挑战，然后在对检测工作中探讨的各个方向进行简要概述之后，详细介绍减轻虚假信息的计算和跨学科方法。
With the rapid increase in access to internet and the subsequent growth in the population of online 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. 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, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
