“It is Luring You to Click on the Link With False Advertising” - Mental Models of Clickbait and Its Impact on User’s Perceptions and Behavior Towards Clickbait Warnings

Abstract

Clickbait, a social engineering attack performed through social media, tricks users through sensationalized or misleading posts into clicking on links that direct them to malicious websites. With the recent boom in social media, clickbait has become a substantial security concern, necessitating efforts from platforms and academia to control it. Despite these attempts, clickbait is effective due to the lack of users’ knowledge. Therefore, we explore user mental models (thought processes about how something works) about clickbait to analyze their deficiencies and their influences on users’ behavior towards clickbait warnings. To this end, we conducted an online study with 770 participants over MTurk to generate user mental models about clickbait and to evaluate the clickbait warnings conveying harm. Our findings suggest that a large portion of users have a simple mental model that fails to comprehend the dangers of clickbait, indicating the importance of warnings in supporting and educating users. Overall, our studies provide valuable insights into understanding the impact of clickbait mental models on users’ online security behavior in social media and offer guidelines for future research in these directions.

Keywords:

• Clickbait
• mental models
• warnings
• quantitative study

Notes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.eccouncil.org/cybersecurity-exchange/ethical-hacking/understanding-preventing-social-engineering-attacks/

2 https://www.eccouncil.org/cybersecurity-exchange/ethical-hacking/understanding-preventing-social-engineering-attacks/

3 https://www.nngroup.com/articles/mental-models/

4 https://www.ueq-online.org/

5 https://www.mturk.com/worker/help

Additional information

Funding

This work was supported by the National Science Foundation under [Grant No. CNS-1949699].

Notes on contributors

Ankit Shrestha

Ankit Shrestha is a PhD candidate and research assistant at the PIXEL (Privacy, desIgn, and user eXperiencE Lab) in Computer Science department of Utah State University. His research interests lie in the boundary of human computer interaction and privacy including a focus on behavior changing interventions.

Arezou Behfar

Arezou Behfar, a Computer Science PhD candidate at Utah State University, specializes in user experience research and usability testing. She engages in collaborative HCI problem-solving at the PIXEL Lab, utilizing methods like design, prototyping, and interviews.

Mahdi Nasrullah Al-Ameen

Mahdi Nasrullah Al-Ameen leads the PIXEL in the Computer Science department of Utah State University. He completed his PhD from University of Texas, Arlington in 2016. His research work focuses on systemizing the human and societal factors that impact people’s secure and privacy-preserving use of a technology.