Authors : Ishika; Hardik Mishra; Mohammad Asim

Volume/Issue : Volume 11 - 2026, Issue 4 - April

---

Google Scholar : https://tinyurl.com/49nzawk4

Scribd : https://tinyurl.com/2vy5zcfh

DOI : https://doi.org/10.38124/ijisrt/26apr862

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : The widespread use of deceptive engagement tactics, known as clickbait, has weakened trust in digital content, particularly on video-sharing platforms such as YouTube. Although automated systems exist to detect sensational headlines, they mostly operate as opaque models that provide no meaningful feedback to users, limiting their ability to develop longterm media awareness. This paper introduces a real-time framework that detects clickbait while simultaneously offering clear, human-readable explanations within the user’s browsing environment. Integrated as a browser extension, the system highlights linguistic patterns such as curiosity gaps and exaggerated phrasing to support informed decision-making.

Keywords : Clickbait Detection, Explainable Systems, Media Literacy, User Behavior Analysis, Browser Extensions, Deceptive Engagement, Human-Computer Interaction, Video-Sharing Platforms.

References :

1. K. Scott, Deceptive clickbait headlines: Relevance, intentions, and lies, Journal of Pragmatics, vol. 218, pp. 71–82, 2023, doi: 10.1016/j.pragma.2023.10.004.
2. Shang, Y. Zhang, Z. Wang, Y. Lai, and Z. Wang, “Online clickbait video detection system independent of video content,” Knowledge-Based Systems, vol. 182, Art. no. 104851, 2019, doi: 10.1016/j.knosys.2019.07.022.
3. T. Liu, Y. Ke, L. Wang, X. Zhang, H. Zhou, and X. Wu, “Detecting clickbait on WeChat using a deep learning model based on semantic and syntactic features, Knowledge-Based Systems, vol. 245, 2022.
4. T. S. Y. Winarto, K. Wijaya, M. A. Faqih, S. Y. Prasetyo, and Y. Muliono, Tackling clickbait with machine learning: A comparative study of binary classification models on YouTube title, Procedia Computer Science, vol. 227, pp. 282–290, 2023, doi: 10.1016/j.procs.2023.10.526.
5. N. Sardana, D. Varshney and S. Luthra, “Enhancing clickbait identification with ensemble machine learning methods, Procedia Computer Science, vol. 258, pp. 599–606, 2025, doi: 10.1016/j.procs.2025.04.294.
6. R. Gothankar, F. Di Troia and M. Stamp, Clickbait detection in YouTube videos, Dept. Comput. Sci., San Jose State Univ., 2021. [Online]. Available: https://arxiv.org/abs/2107.12791
7. P. Mowar, M. Jain, R. Goel and D. K. Vishwakarma, Clickbait on YouTube: Preventing, detecting, and analyzing clickbait using ensemble learning, Dept. Inf. Technol., Delhi Technological Univ., 2021. [Online]. Available: https://arxiv.org/abs/2112.08611
8. W. Navid, Z. A. Uzmi, and Z. A. Qazi, ThumbnailTruth: A multimodal LLM method to identify misleading thumbnails on YouTube, Dept. Comput. Sci., Lahore Univ. Manage. Sci., 2024. [Online]. Available: https://github.com/wajihanaveed/ThumbnailTruth
9. L. Nofar, T. Portal, A. Elbaz, A. Apartsin and Y. Aperstein, “An interpretable benchmark of clickbait detection and tactic attribution, Holon Inst. Technol., 2025. [Online].Available: https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection
10. B. Gamage, A. Labib, A. Joomun, C. H. Lim and K. Wong, Baitradar: A multi-model clickbait detection algorithm using deep learning, in Proc. IEEE ICASSP, 2021. [Online]. Available: https://baitradar.bhanukagamage.com
11. Deng, Y. Zhu, Y. Wang, J. Qiang, Y. Yuan, Y. Li and R. Zhang, Prompt tuning in clickbait detection with text summarization, School Inf. Eng., Yangzhou Univ., 2024. [Online]. Available: https://arxiv.org/abs/2404.11206
12. R. A. Ginga and A. S. Uban, SciTechBaitRO: Clickbait detection in Romanian science and technology news, Univ. Bucharest, 2024. [Online]. Available: https://www.kaggle.com/datasets/andreeaginga/clickbait
13. S. Zannettou, S. Chatzis, K. Papadamou, and M. Sirivianos, The good, the bad and the bait: Detecting and characterizing clickbait on YouTube, in Proc. IEEE Security and Privacy Workshops (SPW), 2018, pp. 63–69, doi: 10.1109/SPW.2018.00018.
14. A. Waingankar and P. P. Anusha, Clickbait detection by using emotions to manipulate using machine learning approaches, Journal of Emerging Technologies and Innovative Research, 2024. [Online]. Available: http://www.jetir.org
15. Agrawal, Clickbait detection with deep learning, in Proc. 2nd Int. Conf. Next Generation computing technologies (NGCT) 2016, p. 268–272, doi: 10.1109/NGCT.2016.7877426.
16. M. Potthast, S. Koepsel, B. Stein and M. Hagen, Clickbait detection, in Advances in Information Retrieval (ECIR), 2016, pp. 810–817, doi: 10.1007/978-3-319-30671-1_72.