@inproceedings{zhou-etal-2026-hidden,
    title = "The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation",
    author = "Zhou, Yuhang  and
      Xiao, Yimin  and
      Ai, Wei  and
      Gao, Ge",
    editor = "Card, Dallas  and
      Field, Anjalie  and
      Keith, Katherine  and
      Mendelsohn, Julia",
    booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
    month = jul,
    year = "2026",
    address = "San Diego",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlpcss-1.19/",
    pages = "322--340",
    ISBN = "979-8-89176-426-2",
    abstract = "Social media platforms have become central to modern communication, yet they also harbor offensive content that challenges platform safety and inclusivity. While prior research has primarily focused on textual indicators of offense, the role of emojis, ubiquitous visual elements in online discourse, remains underexplored. Emojis, despite being rarely offensive in isolation, can acquire harmful meanings through symbolic associations, sarcasm, and contextual misuse. In this work, we systematically examine emoji contributions to offensive Twitter messages, analyzing their distribution across offense categories and how users exploit emoji ambiguity. To address this, we propose an LLM-powered, multi-step moderation pipeline that selectively replaces harmful emojis while preserving the tweet{'}s semantic intent. Human evaluations demonstrate that our approach effectively reduces offensiveness while preserving semantic integrity. Our analysis also reveals heterogeneous effects across offense types, offering nuanced insights for online communication and emoji moderation."
}