@inproceedings{gaur-2026-measuring,
title = "Measuring Public Perceptions of the Social Impacts of Artificial Intelligence in {Y}ou{T}ube Discourse",
author = "Gaur, Aarav",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.25/",
pages = "283--297",
ISBN = "979-8-89176-495-8",
abstract = "Public discourse can show which consequences people associate with Artificial Intelligence (AI), which groups they expect to be affected, and whether they interpret those consequences as benefits or threats. We analyze public perceptions of AI{'}s social impacts across $n=98{,}413$ YouTube comments collected from 2022 to 2025 using a two-stage LLM pipeline that filters neutral and off-topic content before annotating target sentiment, fear, sarcasm, and emotion. Target sentiment achieves Quadratic Weighted Kappa (QWK) $=0.738$ against a human-labeled sample. In exploratory keyword-defined subsets, safety and human-control comments exhibit the highest fear (0.659) and most negative sentiment ($-0.521$), while comments mentioning children, families, and workers are more threat-oriented than those mentioning students and teachers. Negative sentiment remains common among the retained evaluative comments, but whether it rises or falls over time depends on how videos are weighted. Interestingly, sentiment changes near product releases are comparable to those at placebo dates. These results can help direct future research and policy attention toward the concerns most visible in public discourse, especially as a starting point for validation with affected groups."
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<abstract>Public discourse can show which consequences people associate with Artificial Intelligence (AI), which groups they expect to be affected, and whether they interpret those consequences as benefits or threats. We analyze public perceptions of AI’s social impacts across n=98,413 YouTube comments collected from 2022 to 2025 using a two-stage LLM pipeline that filters neutral and off-topic content before annotating target sentiment, fear, sarcasm, and emotion. Target sentiment achieves Quadratic Weighted Kappa (QWK) =0.738 against a human-labeled sample. In exploratory keyword-defined subsets, safety and human-control comments exhibit the highest fear (0.659) and most negative sentiment (-0.521), while comments mentioning children, families, and workers are more threat-oriented than those mentioning students and teachers. Negative sentiment remains common among the retained evaluative comments, but whether it rises or falls over time depends on how videos are weighted. Interestingly, sentiment changes near product releases are comparable to those at placebo dates. These results can help direct future research and policy attention toward the concerns most visible in public discourse, especially as a starting point for validation with affected groups.</abstract>
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%0 Conference Proceedings
%T Measuring Public Perceptions of the Social Impacts of Artificial Intelligence in YouTube Discourse
%A Gaur, Aarav
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F gaur-2026-measuring
%X Public discourse can show which consequences people associate with Artificial Intelligence (AI), which groups they expect to be affected, and whether they interpret those consequences as benefits or threats. We analyze public perceptions of AI’s social impacts across n=98,413 YouTube comments collected from 2022 to 2025 using a two-stage LLM pipeline that filters neutral and off-topic content before annotating target sentiment, fear, sarcasm, and emotion. Target sentiment achieves Quadratic Weighted Kappa (QWK) =0.738 against a human-labeled sample. In exploratory keyword-defined subsets, safety and human-control comments exhibit the highest fear (0.659) and most negative sentiment (-0.521), while comments mentioning children, families, and workers are more threat-oriented than those mentioning students and teachers. Negative sentiment remains common among the retained evaluative comments, but whether it rises or falls over time depends on how videos are weighted. Interestingly, sentiment changes near product releases are comparable to those at placebo dates. These results can help direct future research and policy attention toward the concerns most visible in public discourse, especially as a starting point for validation with affected groups.
%U https://aclanthology.org/2026.nlp4pi-1.25/
%P 283-297
Markdown (Informal)
[Measuring Public Perceptions of the Social Impacts of Artificial Intelligence in YouTube Discourse](https://aclanthology.org/2026.nlp4pi-1.25/) (Gaur, NLP4PI 2026)
ACL