@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Anthropomorphizing AI: A Multi-Label Analysis of Public Discourse on Social Media
%A Raza, Muhammad Owais
%A Meghji, Areej Fatemah
%Y Przybyła, Piotr
%Y Shardlow, Matthew
%Y Colombatto, Clara
%Y Inie, Nanna
%S Proceedings of Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F raza-meghji-2025-anthropomorphizing
%X 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.
%U https://aclanthology.org/2025.ommm-1.8/
%P 64-73
Markdown (Informal)
[Anthropomorphizing AI: A Multi-Label Analysis of Public Discourse on Social Media](https://aclanthology.org/2025.ommm-1.8/) (Raza & Meghji, OMMM 2025)
ACL