| Paper ID | IFS-7.1 |
| Paper Title |
BAITRADAR: A MULTI-MODEL CLICKBAIT DETECTION ALGORITHM USING DEEP LEARNING |
| Authors |
Bhanuka Gamage, Adnan Labib, Aisha Joomun, Chern Hong Lim, KokSheik Wong, Monash University Malaysia, Malaysia |
| Session | IFS-7: Information Hiding, Cryptography and Cybersecurity |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [CYB] Cybersecurity |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Following the rising popularity of YouTube, there is an emerging problem on this platform called clickbait, which provokes users to click on videos using attractive titles and thumbnails. As a result, users ended up watching a video that does not have the content as publicized in the title. This issue is addressed in this study by proposing an algorithm called BaitRadar, which uses a deep learning technique where six inference models are jointly consulted to make the final classification decision. These models focus on different attributes of the video, including title, comments, thumbnail, tags, video statistics and audio transcript. The final classification is attained by computing the average of multiple models to provide a robust and accurate output even in situation where there is missing data. The proposed method is tested on 1,400 YouTube videos. On average, a test accuracy of 98% is achieved with an inference time of ≤ 2s. |
