In the Social Web scenario, large amounts of User-Generated Content (UGC) are diffused through social media often without almost any form of traditional trusted intermediaries. Therefore, the risk of running into misinformation is not negligible. For this reason, assessing and mining the credibility of online information constitutes nowadays a fundamental research issue. Credibility, also referred as believability, is a quality perceived by individuals, who are not always able to discern, with their own cognitive capacities, genuine information from fake one. Hence, in the last years, several approaches have been proposed to automatically assess credibility in social media. Many of them are based on data-driven models, i.e., they employ machine learning techniques to identify misinformation, but recently also model-driven approaches are emerging, as well as graph-based approaches focusing on credibility propagation, and knowledge-based ones exploiting Semantic Web technologies. Three of the main contexts in which the assessment of information credibility has been investigated concern: (i) the detection of opinion spam in review sites, (ii) the detection of fake news in microblogging, and (iii) the credibility assessment of online health-related information. In this article, the main issues connected to the evaluation of information credibility in the Social Web, which are shared by the above-mentioned contexts, are discussed. A concise survey of the approaches and methodologies that have been proposed in recent years to address these issues is also presented.
Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts
University of California, Davis（加州大学戴维斯分校）；Northwestern University（西北大学）；University of Georgia（佐治亚大学）
Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and angry facial images on users' decisions. In study 2, we focus on sources' systematic emotional treatment of specific politicians. Our results show that depending on the political orientation of the source, the cumulative effect of angry facial emoti…
Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots
School of Computing National University of Singapore Singapore Singapore（计算学院新加坡国立大学新加坡新加坡）；Computer Science National University of Singapore Singapore Singapore（计算机科学新加坡国立大学新加坡新加坡）；National University of Singapore（新加坡国立大学）
Credibility Governance: A Social Mechanism for Collective Self-Correction under Weak Truth Signals
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Tsinghua University（清华大学）；University of California, Los Angeles（加州大学洛杉矶分校）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）
Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a result, while social media facilitates an unprecedented ease of access to information, it also introduces a new challenge - that of ascertaining the credibility of shared information. Currently, there is no automated way of determining which news or users are credible and which …
As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying lo…
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Massey University（梅西大学）；Harvard University（哈佛大学）；KeyW Research Lab（KeyW研究实验室）；George Mason University（乔治梅森大学）
The last decade saw the emergence of systematic large-scale replication projects in the social and behavioral sciences, (Camerer et al., 2016, 2018; Ebersole et al., 2016; Klein et al., 2014, 2018; Collaboration, 2015). These projects were driven by theoretical and conceptual concerns about a high fraction of "false positives" in the scientific publications (Ioannidis, 2005) (and a high prevalence of "questionable research practices" (Simmons, Nelson, and Simonsohn, 2011). Concerns about the credibility of research findings are not unique to the behavioral and social sciences; within Computer …
The social web has linked people on a global scale, transforming how we communicate and interact. The massive interconnectedness has created new vulnerabilities in the form of social manipulation and misinformation. As the social web matures, we are entering a new phase, where people share their private feelings and emotions. This so-called social emotional web creates new opportunities for human flourishing, but also exposes new vulnerabilities. To reap the benefits of the social emotional web, and reduce potential harms, we must anticipate how it will evolve and create policies that minimize…
Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal…
The global spread of the novel coronavirus is affected by the spread of related misinformation -- the so-called COVID-19 Infodemic -- that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the preval…
News feeds in virtually all social media platforms include engagement metrics, such as the number of times each post is liked and shared. We find that exposure to these social engagement signals increases the vulnerability of users to misinformation. This finding has important implications for the design of social media interactions in the misinformation age. To reduce the spread of misinformation, we call for technology platforms to rethink the display of social engagement metrics. Further research is needed to investigate whether and how engagement metrics can be presented without amplifying…
Understanding Web Archiving Services and Their (Mis)Use on Social Media
Web存档服务在当今的信息生态系统中扮演着越来越重要的角色，它可以确保信息的持续可用性，也可以故意缓存可能被删除或移除的内容。其中，自2001年以来，Wayback Machine一直在主动存档大量网页的版本，而像archive.is这样的新服务允许用户创建特定网页的按需快照，这些快照可以作为可以在Web上共享的时间胶囊。在本文中，我们对Web存档服务及其在社交媒体上的使用进行了大规模分析，揭示了这个生态系统中涉及的参与者，存档的内容以及如何共享。我们抓取和研究：1）来自archive.is的21 M URL，跨越近两年，2）356 K archive.is加上391 K Wayback Machine URL，这些URL在四个社交网络上共享：Reddit，Twitter，Gab和4chan的Politically Incorrect board（/pol/）超过14个月。我们观察到，新闻和社交媒体帖子是最常见的存档内容类型，这可能是由于它们被认为是短暂的和/或有争议的性质。此外，存档服务的URL在Reddit和4chan的“边缘”社区中广泛共享，以保留可能有争议的内容。最后，我们发现有证据表明，版主推动甚至强迫用户使用档案，而不是直接链接，用于具有相反意识形态的新闻来源，这可能会剥夺他们的广告收入。
Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engin…
