Customized video filtering on YouTube
Vishal Anand, Ravi Shukla, Ashwani Gupta, Abhishek Kumar

TL;DR
This paper presents a system for identifying inappropriate YouTube videos to help corporations advertise safely, addressing the platform's current ineffective content filtering methods.
Contribution
It introduces a customizable framework for large-scale detection of inappropriate videos on YouTube, enhancing ad placement safety for corporations.
Findings
YouTube still hosts many disturbing videos despite existing measures.
Current detection methods are ineffective in timely filtering inappropriate content.
The proposed system offers an effective add-on solution for content filtering.
Abstract
Inappropriate and profane content on social media is exponentially increasing and big corporations are becoming more aware of the type of content on which they are advertising and how it may affect their brand reputation. But with a huge surge in content being posted online it becomes seemingly difficult to filter out related videos on which they can run their ads without compromising brand name. Advertising on youtube videos generates a huge amount of revenue for corporations. It becomes increasingly important for such corporations to advertise on only the videos that don't hurt the feelings, community or harmony of the audience at large. In this paper, we propose a system to identify inappropriate content on YouTube and leverage it to perform a first of its kind, large scale, quantitative characterization that reveals some of the risks of YouTube ads consumption on inappropriate…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Spam and Phishing Detection · Advanced Malware Detection Techniques
