Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach
作者：
Highlights：
• A list of requirements for new human-centered content analyses.
• 3 perspectives for subjective content perception that affect annotation procedures.
• New methods for offensive content recognition that respect full personalization.
• Reasoning process based on personal behavior implementing human-centered approaches.
• Multimodal transformer-based human-centered data representations.
• Combining text, metadata, annotator demographics, and annotator behavior.
• Evidence that human-centered classification approaches outperform classic methods.
摘要
•A list of requirements for new human-centered content analyses.•3 perspectives for subjective content perception that affect annotation procedures.•New methods for offensive content recognition that respect full personalization.•Reasoning process based on personal behavior implementing human-centered approaches.•Multimodal transformer-based human-centered data representations.•Combining text, metadata, annotator demographics, and annotator behavior.•Evidence that human-centered classification approaches outperform classic methods.
论文关键词：Hate speech,Offensive content,Human-centered NLP,Multimodal deep learning,Personalization,Subjective content perception,Annotator agreement
论文评审过程：Received 23 December 2020, Revised 11 March 2021, Accepted 14 May 2021, Available online 3 June 2021, Version of Record 3 June 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102643
