As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users' voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.
翻译:随着民间话语日益在网上出现,线上社区共享新闻的错误和两极分化变得日益成为我们社会上真实世界的危害问题。研究在线新闻分享的规模具有挑战性,因为成千上万个社区的用户共享了大量内容。因此,现有研究主要侧重于特定社区或具体干预措施,例如禁令。然而,了解错误和分化的普遍程度和蔓延程度,在数千个在线社区中,对于制定治理战略、干预措施和社区设计而言,更为广泛的程度和扩散至关重要。在这里,我们进行关于重编新闻分享的最大研究,分析超过5.5亿个链接长达4年。我们使用媒体Bias/Fact Check的无党派新闻来源评级来说明其政治偏差和事实性。我们发现,与左派社区相比,右派社区在政治偏见和分化方面的差异增加了105 %, 与相对偏差程度较低的来源的联系也更多。我们发现,重新编辑用户的投票和重新分享的行为通常会减少极端偏差和低级内容的准确性程度。我们发现,最近这种事实性或低级内容的公开性程度比事实性程度要少于20 %。