AOMD: An analogy-aware approach to offensive meme detection on social media
作者：
Highlights：
• We define a novel problem of detecting offensive analogy memes on online social media.
• We design a principled scheme to jointly exploit multi-modal content in meme posts.
• We explicitly model the relations between visual and textual content in memes.
• We collect two real-world datasets on Reddit and Gab to extensively evaluate the detection performance.
• Evaluation results show significant performance gains of the proposed solution.
摘要
•We define a novel problem of detecting offensive analogy memes on online social media.•We design a principled scheme to jointly exploit multi-modal content in meme posts.•We explicitly model the relations between visual and textual content in memes.•We collect two real-world datasets on Reddit and Gab to extensively evaluate the detection performance.•Evaluation results show significant performance gains of the proposed solution.
论文关键词：Offensive meme,Analogy-aware,Multi-modal learning
论文评审过程：Received 3 March 2021, Revised 1 June 2021, Accepted 14 June 2021, Available online 25 June 2021, Version of Record 25 June 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102664
