Graduation Year

2021

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Business Administration

Major Professor

Balaji Padmanabhan, Ph.D.

Committee Member

Sunil Mithas, Ph.D.

Committee Member

He Zhang, Ph.D.

Committee Member

Anand Kumar, Ph.D.

Keywords

Misinformation, Agent-Based Modeling, Health Information Quality, AI Governance

Abstract

Fake news has been considered one of the most challenging problems in the last few years. The effects of spreading fake news over social media platforms are widely observed across the globe as the depth and velocity of fake news reach far more than real news (Vosoughi et al., 2018). The plan for the following dissertation is to investigate the mass spread of fake news across social media and propose a framework to fight the spread of fake news by mixing preventive methods that could hinder the overall percentage of fake news sharing. We plan to create a study on the social network by recreating a complex adaptive system (CAS) that mimics a real social network; then, we use agent-based modeling to simulate the flow of news sharing. Finally, we evaluate appropriate governance policies and apply the appropriate ones to reduce the spread of fake news. This dissertation contributes to online misinformation research in multiple areas: First, we present a framework to fight fake news, which social media platforms and researchers interested in the same domain can utilize. We propose that this framework allows it to be reproduced by any entity that has a social network. Second, we present an agent-based modeling design of fake news dissemination on a social network that can be used for fake news research that uses complex systems properties. Finally, we aim to contribute our work in healthcare to analyze further fake news related to the domain. Since much information attributed to cancer-related videos publicly available on YouTube is surrounded by uncertainty, we aim to systematically find systematic approaches that could reduce misinformation related to YouTube cancer videos.

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