Clickbait Detection in YouTube Videos
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state-of-the-art machine learning techniques using a variety of textual features.
Code (0)
등록된 구현이 없습니다.
Tasks
Clickbait DetectionSimilar Papers 제목 키워드 기반
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
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 emerg…
Clickbait DetectionBaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis
This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset co…
ClassificationClickbait DetectionMultilingual NLPYTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories
Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets…
Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users globally. These platforms expose users to harmful content, ranging from clickbait or physical harms to misinformation or online …
Binary ClassificationMisinformationtext annotationDetection and Analysis of Content Creator Collaborations in YouTube Videos using Face- and Speaker-Recognition
This work discusses and implements the application of speaker recognition for the detection of collaborations in YouTube videos. CATANA, an existing framework for detection and analysis of YouTube collaborations, is util…
Active Speaker DetectionFace RecognitionSpeaker Recognition