Towards reliable online clickbait video detection: A content-agnostic approach
| Autor: | Dong Wang, Lanyu Shang, Shuyue Lai, Michael Wang, Daniel Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Scheme (programming language) Focus (computing) Information Systems and Management Multimedia Computer science Video detection ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Social and Information Networks 02 engineering and technology computer.software_genre Computer Science - Information Retrieval Management Information Systems Artificial Intelligence 020204 information systems Content (measure theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer Information Retrieval (cs.IR) Software computer.programming_language |
| Zdroj: | Knowledge-Based Systems. 182:104851 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2019.07.022 |
| Popis: | Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos. In this paper, we develop a novel content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by exploring the comments from the audience who watched the video. Different from existing solutions, OVCP does not directly analyze the content of the video and its pre-click information (e.g., title and thumbnail). Therefore, it is robust against sophisticated content creators who often generate clickbait videos that can bypass the current clickbait detectors. We evaluate OVCP with a real-world dataset collected from YouTube. Experimental results demonstrate that OVCP is effective in identifying clickbait videos and significantly outperforms both state-of-the-art baseline models and human annotators. Accepted by Knowledge-Based Systems |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect