The history of journalism and news diffusion is tightly coupled with the effort to dispel hoaxes, misinformation, propaganda, unverified rumours, poor reporting, and messages containing hate and divisions. With the explosive growth of online social media and billions of individuals engaged with consuming, creating, and sharing news, this ancient problem has surfaced with a renewed intensity threatening our democracies, public health, and news outlets credibility. This has triggered many researchers to develop new methods for studying, understanding, detecting, and preventing fake-news diffusion; as a consequence, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines to struggle in search of open problems and most relevant trends. The aim of this survey is threefold: first, we want to provide the researchers interested in this multidisciplinary and challenging area with a network-based analysis of the existing literature to assist them with a visual exploration of papers that can be of interest; second, we present a selection of the main results achieved so far adopting the network as an unifying framework to represent and make sense of data, to model diffusion processes, and to evaluate different debunking strategies. Finally, we present an outline of the most relevant research trends focusing on the moving target of fake-news, bots, and trolls identification by means of data mining and text technologies; despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that forthcoming computational approaches to prevent fake news from polluting the social media must be developed using hybrid and up-to-date methodologies.
翻译:新闻和新闻传播的历史与消除骗局、错误信息、宣传、未经核实的谣言、不良报道和含有仇恨和分歧的信息的努力紧密地联系在一起。随着在线社交媒体和数十亿参与消费、创造和分享新闻的个人爆炸性增长,这一古老问题又重新出现,威胁着我们的民主国家、公共卫生和新闻媒体的信誉。这促使许多研究人员制定新的研究、理解、发现和防止假新闻传播的方法;因此,在相对较短的时期内发表了数千份中期科学论文,使不同学科的研究人员努力寻找公开的问题和最相关的趋势。这次调查的目的有三重:第一,我们要向对这个多学科和富有挑战性的领域感兴趣的研究人员提供基于网络的现有文献分析,以协助他们对可能令人感兴趣的论文进行直观探讨;第二,我们选择了迄今为止取得的主要成果,将网络作为反映和认识数据的统一框架,模拟传播过程,并评价不同的解析战略。最后,我们提出一个有关传统研究趋势的大纲,即:我们利用最具有相关性的、最具有历史意义的网络和最新研究趋势的网络,将数据集中在最新数据上;我们利用最相关的统计和最新的数据计算方法,从模拟的统计方法,必须从模拟的统计到虚拟的统计方法,将数据推入未来。