@inproceedings{raza-meghji-2025-anthropomorphizing,
    title = "Anthropomorphizing {AI}: A Multi-Label Analysis of Public Discourse on Social Media",
    author = "Raza, Muhammad Owais  and
      Meghji, Areej Fatemah",
    editor = "Przyby{\l}a, Piotr  and
      Shardlow, Matthew  and
      Colombatto, Clara  and
      Inie, Nanna",
    booktitle = "Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ommm-1.8/",
    pages = "64--73",
    abstract = "As the anthropomorphization of AI in public discourse usually reflects a complex interplay of metaphors, media framing, and societal perceptions, it is increasingly being used to shape and influence public perception on a variety of topics. To explore public perception and investigate how AI is personified, emotionalized, and interpreted in public discourse, we develop a custom multi-labeled dataset from the title and description of YouTube videos discussing artificial intelligence (AI) and large language models (LLMs). This was accomplished using a hybrid annotation pipeline that combined human-in-the-loop validation with AI assisted pre-labeling. This research introduces a novel taxonomy of narrative and epistemic dimensions commonly found in social media content on AI / LLM. Employing two modeling techniques based on traditional machine learning and transformer-based models for classification, the experimental results indicate that the fine-tuned transformer models, particularly AnthroRoBERTa and AnthroDistilBERT, generally outperform traditional machine learning approaches in anthropomorphization focused classification."
}