The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence
The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence
复制标题
基于人工智能的可信度指标对社会影响下错误信息检测和传播的影响
DOI：
10.1145/3555562
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发表时间：
2022
影响因子：
--
通讯作者：
Yin, Ming
中科院分区：
文献类型：
--
作者：
Lu, Zhuoran;Li, Patrick;Wang, Weilong;Yin, Ming
社交媒体上的错误信息已经成为一个严重的问题。用可信度指标标记新闻故事，可能是由人工智能模型生成的，是帮助人们对抗错误信息的一种方法。在本文中，我们报告了两个随机实验的结果，旨在了解基于人工智能的可信度指标对人们对新闻的看法和参与的影响，当人们受到社会影响时，他们对新闻的判断受到其他人的影响。我们发现，基于人工智能的可信度指标的存在促使人们将他们对新闻真实性的信念与人工智能模型的预测保持一致，而不管其正确性如何，从而改变了人们检测错误信息的准确性。然而，基于人工智能的可信度指标显示，当存在社会影响时，影响人们对真实的新闻或假新闻的参与的影响有限。最后，研究表明，当存在社会影响时，基于人工智能的可信度指标对错误信息的检测和传播的影响要大于不存在社会影响时，当这些指标在人们形成自己对新闻的判断之前提供给他们时。最后，我们为更好地利用人工智能来打击错误信息提供了启示。
Misinformation on social media has become a serious concern. Marking news stories with credibility indicators, possibly generated by an AI model, is one way to help people combat misinformation. In this paper, we report the results of two randomized experiments that aim to understand the effects of AI-based credibility indicators on people's perceptions of and engagement with the news, when people are under social influence such that their judgement of the news is influenced by other people. We find that the presence of AI-based credibility indicators nudges people into aligning their belief in the veracity of news with the AI model's prediction regardless of its correctness, thereby changing people's accuracy in detecting misinformation. However, AI-based credibility indicators show limited impacts on influencing people's engagement with either real news or fake news when social influence exists. Finally, it is shown that when social influence is present, the effects of AI-based credibility indicators on the detection and spread of misinformation are larger as compared to when social influence is absent, when these indicators are provided to people before they form their own judgements about the news. We conclude by providing implications for better utilizing AI to fight misinformation.
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DOI：
10.1073/pnas.2020043118
发表时间：
2021-02-02
影响因子：
11.1
作者：
Brashier NM;Pennycook G;Berinsky AJ;Rand DG
通讯作者：
Rand DG
社交网络结构对在线社区成长和生存的影响
DOI：
--
发表时间：
2019
期刊：
International Conference on Advances in Social Networks Analysis and Mining
影响因子：
--
作者：
Sho Tsugawa;S. Niida
通讯作者：
S. Niida
DOI：
10.1145/3411764.3445562
发表时间：
2021-05
期刊：
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
影响因子：
--
作者：
Zhuoran Lu;Ming Yin
通讯作者：
Zhuoran Lu;Ming Yin
DOI：
10.2139/ssrn.3035384
发表时间：
2019-08
期刊：
Political Communication eJournal
影响因子：
--
作者：
Gordon Pennycook;Adam Bear;Evan T. Collins;David G. Rand
通讯作者：
Gordon Pennycook;Adam Bear;Evan T. Collins;David G. Rand
用于假新闻检测的混合机器-人群方法
DOI：
10.1109/cic.2018.00048
发表时间：
2018
期刊：
2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)
影响因子：
--
作者：
Shaban Shabani;M. Sokhn
通讯作者：
M. Sokhn