ViSSa: Recognizing the appropriateness of videos on social media with on-demand crowdsourcing

Abstract

The recent significant growth of social media has brought the attention of researchers toward monitoring the enormous amount of streaming data using real-time approaches. This data may appear in different forms like streaming text, images, audio, videos, etc. In this paper, we address the problem of deciding the appropriateness of streaming videos with the help of on-demand crowdsourcing. We propose a novel crowd-powered model ViSSa, which is an open crowdsourcing platform that helps to automatically detect appropriateness of the videos getting uploaded online through employing the viewers of existing videos. The proposed model presents a unique approach of not only identifying unsafe videos but also detecting the portion of inappropriateness (in terms of platform’s vulnerabilities). Our experiments with 47 crowd contributors demonstrate the effectiveness of the proposed approach. On the designed ViSSa platform, 18 safe videos are initially posted. After getting access, 20 new videos are added by different users. These videos are assessed (and marked as safe or unsafe) by users and finally with judgment analysis a consensus judgment is obtained. The approach detects the unsafe videos with high accuracy (95%) and point out the portion of inappropriateness. Interestingly, changing the mode of video segment allocation (homogeneous and heterogeneous) is found to have a significant impact on the viewers’ feedback. However, the proposed approach performs consistently well in different modes of viewing (with varying diversity of opinions), and with any arbitrary video size and type. The users are found to be motivated by their sense of responsibility. This paper also highlights the importance of identifying spammers through such models.