Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal an increased level of public knowledge. Unlike traditional media channels, social networks also facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviours, attitudes and beliefs of the public, and ultimately can seriously endanger the democratic processes. Limiting false information's negative impact through early detection and control of extensive spread presents the main challenge facing researchers today. In this survey paper, we extensively analyse a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets. Finally, we evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.

社交网络的无所不在和易用性彻底改变了当今世界信息的生成和分发。然而，容易获得信息并不等于公共知识水平的提高。与传统媒体渠道不同，社交网络还有助于更快、更广泛地传播虚假信息和错误信息。虚假信息的病毒式传播严重影响了公众的行为、态度和信仰，最终会严重危及民主进程。通过早期检测和控制广泛传播来限制虚假信息的负面影响是当今研究人员面临的主要挑战。在本调查论文中，我们广泛分析了现有文献中用于早期检测假新闻的各种不同解决方案。更确切地说，我们研究了机器学习 (ML) 模型，用于识别和分类假新闻、在线假新闻检测竞赛、统计输出以及一些可用数据集的优缺点。最后，我们评估了可用于检测和减少假新闻的在线 Web 浏览工具，并提出了一些开放的研究挑战。