CRII: CHS: Empirical and Design Investigations to Address Misleading Online News in Social Media

本研究采用社会计算和以人为本的方法来理解网络新闻背景下人与技术之间的关系。 它专门调查传播虚构故事的在线新闻来源，探索用户如何在社交媒体上与这些来源互动，并研究如何推动用户成为更有意识的在线新闻消费者。 本研究旨在通过关注以下问题提供解决数字错误信息的新视角：我们如何建立主流来源和误导性来源的在线新闻之间的差异？我们如何才能促使人们成为更谨慎、更有意识的在线新闻消费者？这项研究将沿着沿着两条共生的调查路线进行。第一个是对误导性网络新闻来源的实证调查，以深入了解他们的行为沿着以下线索：主题和写作风格与主流来源的差异，用户参与的区别，以及相应的时间变化。这项调查将基于以下数据：一份专业策划的在线新闻来源清单，沿着由专家事实核查人员提供的可信度标签，以及这些新闻来源在至少一年的时间内发出的推文。这项研究的第二个重点是探索设计干预措施，以提高人们在社交媒体网站上阅读新闻时的意识。 具体来说，它将调查Twitter上的两类设计轻推。强调，第一个干预将促使用户反思某些新闻帖子中存在的模糊性和不确定性。Emphasize将自动检测来自主流来源的社交媒体新闻帖子是否受到质疑，并为新闻读者突出显示这些问题推文。第二个干预措施是“淡化”，每当新闻帖子来自误导性来源时，就会触发这一干预措施，使该帖子不那么显眼，以尽量减少对误导性在线新闻的曝光。伴随这些干预措施部署的以人为本的评估将提供有关用户体验的定性和定量证据，以及有关这些干预措施有效性的衡量标准。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This research employs social computing and human-centered approaches to understand the relationship between people and technology in the context of online news. It specifically investigates online news sources that propagate fabricated stories, explores how users engage with these sources on social media, and examines ways to nudge users to be more conscious consumers of online news. This investigation aims to provide new perspectives that address digital misinformation by focusing on the following questions: How can we establish differences between mainstream sources and misleading sources of online news? How can we nudge people to be more careful and conscious consumers of online news? The research will be conducted along two symbiotic lines of inquiry. The first involves empirical investigation of misleading online news sources to develop a deep understanding of their behavior along the following threads: topical and writing style differences from mainstream sources, user engagement distinctions, and the corresponding temporal changes. This inquiry will be based on the following data: a professionally curated list of online news sources along with their credibility labels from expert fact-checkers, and tweets sent out by these news sources over a period of at least a year. The second thrust of this research focuses on exploring design interventions to increase people's awareness while they read news on social media sites. Specifically, it will investigate two classes of design nudges on Twitter. The first intervention, emphasize, will nudge users to reflect on the ambiguity and uncertainty present in certain news posts. Emphasize will automatically detect whether a social media news post from a mainstream source has been questioned and highlight those question tweets for the news reader. The second intervention, de-emphasize, will be triggered whenever news posts originate from misleading sources, to make that post less visible in an attempt to minimize exposure to misleading online news. The human-centered evaluations that accompany the deployment of these interventions will provide qualitative and quantitative evidence about user experiences, as well as measurements about the efficacy of these interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.