Date of Award:
8-2025
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Computer Science
Committee Chair(s)
Mahdi Nasrullah Al-Ameen
Committee
Mahdi Nasrullah Al-Ameen
Committee
Chad Mano
Committee
Soukaina Filali Boubrahimi
Committee
Shuhan Yuan
Committee
Kristin Searle
Abstract
Clickbait is misleading or exaggerated content on social media that tricks people into clicking on links by making them curious. For instance, posts that use headlines such as “This is the worst day to visit a restaurant”. These clickbait posts can lead to fake news, wrong information, and even harmful websites. Even though many people know clickbait can be risky, they often still fall for it. Existing tools to stop clickbait don’t always consider how people think, the situations they are in, or their different needs. This research looks at how people understand and react to clickbait, and develops different ways to protect them through four studies. The first study explores how users think about clickbait and finds many common misunderstandings that make people more likely to be tricked and less likely to pay attention to warnings. The second study focuses on clickbait that uses personal information in social media to make posts more convincing. The third study looks at teenagers, who are especially vulnerable to clickbait but often left out of safety research. By involving teens in designing solutions, it was found that peer pressure matters a lot and that teens prefer helpful advice over strict rules. The fourth study examines clickbait in short videos like reels and stories. These formats encourage quick, impulsive viewing, making it easier to fall for clickbait. In the study, new strategies were developed that fit the fast nature of these videos and help users stay cautious. Together, these studies offer practical tools to keep people safe from clickbait across different social media content and user groups.
Checksum
eac7783df81aefa2f21d865c95443c42
Recommended Citation
Shrestha, Ankit, "Beyond the Click: Investigating Mental Models, Targeted Attacks, And Behavioral Interventions Against Clickbait" (2025). All Graduate Theses and Dissertations, Fall 2023 to Present. 623.
https://digitalcommons.usu.edu/etd2023/623
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