Trustworthy Misinformation Mitigation with Soft Information Nudging

Benjamin D. Horne, Mauricio G. Gruppi, Sibel Adali
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引用次数: 8

Abstract

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.
可信赖的错误信息缓解与软信息推动
打击虚假信息的研究报告了许多负面结果:事实可能不会改变人们的想法,尤其是当它们来自不可信的来源时。个人可以无视可信来源的谎言,也可以为其辩护。社交推荐算法使这个问题变得更糟,这些算法有助于放大阴谋论和确认个人偏见的信息,因为公司努力优化点击量,关注个人自身价值观和公共利益。结果,更微妙的声音和事实被对更好的信息来源的信任不断侵蚀所淹没。大多数缓解错误信息的技术都假定,对低真实性信息进行抹黑、过滤或降级将有助于新闻消费者做出更好的信息决策。然而,这些负面结果表明,一些新闻消费者,特别是极端或阴谋新闻消费者不会得到帮助。我们认为,在这种背景下,打击错误信息的技术解决方案不应该简单地寻求事实或诋毁坏消息来源,而是使用更微妙的推动来实现更好的信息消费。反复接触这样的推动有助于促进对更好的信息来源的信任,从长远来看也会改善社会结果。在本文中,我们将讨论可以帮助我们开发这种方法的技术解决方案,并介绍一种称为“信任推动”的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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