On the Impact of Showing Evidence from Peers in Crowdsourced Truthfulness Assessments

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiechen Xu, Lei Han, Shazia Sadiq, Gianluca Demartini
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引用次数: 0

Abstract

Misinformation has been rapidly spreading online. The common approach to deal with it is deploying expert fact-checkers that follow forensic processes to identify the veracity of statements. Unfortunately, such an approach does not scale well. To deal with this, crowdsourcing has been looked at as an opportunity to complement the work done by trained journalists. In this paper, we look at the effect of presenting the crowd with evidence from others while judging the veracity of statements. We implement various variants of the judgment task design to understand if and how the presented evidence may or may not affect the way crowd workers judge truthfulness and their performance. Our results show that, in certain cases, the presented evidence and the way in which it is presented may mislead crowd workers who would otherwise be more accurate if judging independently from others. Those who make appropriate use of the provided evidence, however, can benefit from it and generate better judgments.

论在众包真实性评估中展示同行证据的影响
错误信息在网上迅速传播。常见的应对方法是部署专家事实核查人员,按照取证流程识别言论的真实性。遗憾的是,这种方法不能很好地扩展。为了解决这个问题,众包被视为补充训练有素的记者工作的一个机会。在本文中,我们研究了在判断言论的真实性时,向人群展示他人证据的效果。我们实施了各种变体的判断任务设计,以了解呈现的证据是否会影响或如何影响人群工作者判断真实性的方式及其表现。我们的结果表明,在某些情况下,提供的证据和提供证据的方式可能会误导人群工作者,否则他们在独立于他人进行判断时会更加准确。然而,那些适当利用所提供证据的人却能从中受益,做出更好的判断。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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