Identifying Unsafe Videos on Online Public Media using Real-time Crowdsourcing

TL;DR

This paper proposes a real-time crowdsourcing approach to identify unsafe videos on online public media, addressing the challenge of analyzing streaming multimedia content for appropriateness.

Contribution

It introduces a novel real-time crowdsourcing framework specifically designed for assessing the safety of streaming videos in public media platforms.

Findings

Effective detection of unsafe videos demonstrated

Real-time processing achieves timely moderation

Crowdsourcing enhances accuracy in content evaluation

Abstract

Due to the significant growth of social networking and human activities through the web in recent years, attention to analyzing big data using real-time crowdsourcing has increased. This data may appear in the form of streaming images, audio or videos. In this paper, we address the problem of deciding the appropriateness of streaming videos in public media with the help of crowdsourcing in real-time.