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

作者：

Highlights：

• We propose a novel crowd-powered model to detect appropriateness of the videos on social media.

• Our experiments with 47 crowd contributors demonstrate the effectiveness of the proposed approach.

• The approach detects the unsafe videos with high accuracy (95%) and point out the portion of inappropriateness.

摘要

•We propose a novel crowd-powered model to detect appropriateness of the videos on social media.•Our experiments with 47 crowd contributors demonstrate the effectiveness of the proposed approach.•The approach detects the unsafe videos with high accuracy (95%) and point out the portion of inappropriateness.

论文关键词：Crowdsourcing,Streaming data,Video analysis,Judgment analysis

论文评审过程：Received 26 June 2019, Revised 29 November 2019, Accepted 21 December 2019, Available online 21 January 2020, Version of Record 21 January 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2019.102189