AI-based credibility indicators for fighting fake news
10-26-2022
Assistant Professor Ming Yin won a Best Paper Award for The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence at the ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW).
Can AI be used to fight misinformation on social media?
Perpetually ardent topics on social media, like voting or vaccination, can be susceptible to the wide and rapid spread of misinformation. Potentially causing confusion and panic among people and even misleading people’s decisions in the real world, fake news is a huge problem.
Using AI-based credibility indicators to tag social posts is a way to provide instant feedback on the legitimacy of the content in social posts.
Assistant Professor Ming Yin and her students; Zhuoran Lu, Patrick Li, and Weilong Wang recently won a Best Paper Award for The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence at the ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW).
Professor Ming Yin and members of her research team are determining what instances work best for using AI-based credibility indicators on social posts. One common approach taken by social media platforms is to have third-party fact-checkers review information and put warning labels on those content rated as false.
However, the limited scalability of manual fact-checking has prompted researchers and practitioners alike to explore alternative methods for signaling the credibility of online information, such as through automated AI-based technologies
The problem with evaluating the effects of AI credibility indicators on people’s perceptions of and engagement with the news on its own is that it doesn’t take into account how a person’s “social influence” – the process by which an individual's attitudes, beliefs or behavior are modified by the presence or action of others – has an effect.
In two randomized experiments, AI credibility indicators were shown to have an effect on people’s perceptions of and engagement with the news, even when people are under social influence.
“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,” said Yin. She added, “regardless of its correctness, thereby it changes people's accuracy in detecting misinformation.”
However, AI credibility indicators show limited impacts on influencing people’s engagement with real or fake news when social influences exist for users.
In addition, when social influence is present, the effects of AI credibility indicators on detection and spread of information are found to be larger as compared to when social influence is absent, if these indicators are provided to people before they form their own judgment about the news.
When taking all of the influences into context, the researchers provided implications for better utilization of AI to fight misinformation found in social posts.
Yin and colleagues will present their paper at the 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW), Nov 8-22, 2022.
CSCW is the premier venue for research in the design and use of technologies that affect groups, organizations, communities, and networks. Bringing together top researchers and practitioners, CSCW explores the technical, social, material, and theoretical challenges of designing technology to support collaborative work and life activities.
The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence
Zhuoran Lu (Purdue University), Patrick Li (Purdue University), Weilong Wang (Purdue University), Ming Yin (Purdue University)
Abstract
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.
About the Department of Computer Science at Purdue University
Founded in 1962, the Department of Computer Science was created to be an innovative base of knowledge in the emerging field of computing as the first degree-awarding program in the United States. The department continues to advance the computer science industry through research. US News & Reports ranks Purdue CS #20 and #16 overall in graduate and undergraduate programs respectively, seventh in cybersecurity, 10th in software engineering, 13th in programming languages, data analytics, and computer systems, and 19th in artificial intelligence. Graduates of the program are able to solve complex and challenging problems in many fields. Our consistent success in an ever-changing landscape is reflected in the record undergraduate enrollment, increased faculty hiring, innovative research projects, and the creation of new academic programs. The increasing centrality of computer science in academic disciplines and society, and new research activities - centered around data science, artificial intelligence, programming languages, theoretical computer science, machine learning, and cybersecurity - are the future focus of the department. cs.purdue.edu
Writer: Emily Kinsell, emily@purdue.edu
Source: Ming Yin, mingyin@purdue.edu