With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets’ credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information overload problem. The aim of this survey is threefold: (1) we present the results of a network-based analysis of the existing multidisciplinary literature to support the search for relevant trends and central publications; (2) we describe the main results and necessary background to attack the problem under a computational perspective; (3) we review selected contributions using network science as a unifying framework and computational linguistics as the tool to make sense of the shared content. Despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that those interested in the area of fake news should be aware of crucial aspects of both disciplines.

随着在线社交媒体的爆炸式增长，干扰新闻传播的信息混乱这一古老问题再次浮出水面，威胁着我们的民主、公共卫生和新闻媒体的可信度。因此，在较短的时间内发表了数千篇科学论文，使不同学科的研究人员都面临着信息过载的问题。本次调查的目的有三个：(1) 我们展示了对现有多学科文献的基于网络的分析结果，以支持搜索相关趋势和主要出版物；(2) 我们从计算的角度描述了解决问题的主要结果和必要的背景；(3) 我们使用网络科学作为统一框架和计算语言学作为理解共享内容的工具来审查选定的贡献。尽管研究计算语言学和网络的学者传统上属于不同的科学界，但我们希望那些对假新闻领域感兴趣的人应该了解这两个学科的关键方面。