Document Type
Article
Author ORCID Identifier
A. Shrestha https://orcid.org/0000-0002-9012-6146
A. Flood https://orcid.org/0009-0004-3872-7990
B. Hackler https://orcid.org/0009-0000-2144-9040
A. Behfar https://orcid.org/0000-0001-8547-7868
M. N. Al-Ameen https://orcid.org/0000-0002-5764-2253
Journal/Book Title/Conference
18th International Technology, Education and Development Conference
Publisher
IATED
Location
Valencia, Spain
Publication Date
3-4-2024
Journal Article Version
Accepted Manuscript
First Page
1794
Last Page
1804
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Abstract
Clickbait refers to sensationalized or misleading post on social networking sites (e.g., Facebook) that can trick users into clicking on malicious links. Clickbait is often used in cyberattacks, especially to conduct 'social engineering attacks' that direct users to malicious websites, resulting in disclosure of users' personal information or installing malicious software (i.e., malware). Thus, clickbait has become a major security concern with the recent boom in social media use. Therefore, security education has become necessary more than ever for the safe and secure use of social media, where there is dearth in security education literature to explore how we could leverage the learning science principles, and the mental models (thought processes about how something works) of users in designing educational contents. We begin to address this gap in our work, where we conducted two online studies over Amazon Mechanical Turk (MTurk), recruiting a total of 834 participants. Our first study aimed to understand the existing mental models of clickbait among social media users; we derived six mental models from our study, which led to the design of security education materials (treatment condition) integrating user mental models with learning science principles. We then conducted our second online study to evaluate treatment condition with the baseline educational content. Our findings denote the efficacy of leveraging user mental models in security education design, and unveil the potentials of integrating learning science principles into the design process. Based on our findings, we provide guidelines for future education research in these directions.
Recommended Citation
A. Shrestha, A. Flood, B. Hackler, A. Behfar, M.N. Al-Ameen (2024) TOWARDS THE DESIGN AND EVALUATION OF CLICKBAIT EDUCATION CONTENT: LEVERAGING USER MENTAL MODELS AND LEARNING SCIENCE PRINCIPLES, INTED2024 Proceedings, pp. 1794-1804.
