@inproceedings{aldous-etal-2026-ai,
title = "{AI}-Sustainability Discourse Across Social Media: Sentiment, Topics, and Engagement on {L}inked{I}n, {X}, and Bluesky",
author = "Aldous, Kholoud Khalil and
Biswas, Md. Rafiul and
Bessghaier, Mabrouka and
Ibrahim, Shimaa and
Attia, Kais and
Zaghouani, Wajdi",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.152/",
pages = "3004--3018",
ISBN = "979-8-89176-444-6",
abstract = "We present the first cross-platform computational analysis of AI-sustainability discourse across LinkedIn, X, and Bluesky, using 52,102 posts collected over 15 months. Combining RoBERTa-based sentiment classification with BERTopic topic modeling, we identify 36 topics and compare sentiment, topic distributions, and engagement across platforms. Overall, 43.98{\%} of posts are positive, 40.16{\%} neutral, and 15.86{\%} negative. Sentiment varies by topic rather than by platform: corporate and AI-potential themes are mostly positive, while resource-cost topics carry the highest negative sentiment. Topic distributions differ significantly across platforms: LinkedIn focuses on corporate sustainability, X on hardware and cryptocurrency, and Bluesky on environmental resource costs. Niche topics consistently attract more engagement than high-volume ones. These findings have implications for how organizations and policymakers communicate about AI-sustainability, as topic framing and platform audience composition shape public discourse more than platform selection alone. We release the annotated dataset to support future research."
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%0 Conference Proceedings
%T AI-Sustainability Discourse Across Social Media: Sentiment, Topics, and Engagement on LinkedIn, X, and Bluesky
%A Aldous, Kholoud Khalil
%A Biswas, Md. Rafiul
%A Bessghaier, Mabrouka
%A Ibrahim, Shimaa
%A Attia, Kais
%A Zaghouani, Wajdi
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F aldous-etal-2026-ai
%X We present the first cross-platform computational analysis of AI-sustainability discourse across LinkedIn, X, and Bluesky, using 52,102 posts collected over 15 months. Combining RoBERTa-based sentiment classification with BERTopic topic modeling, we identify 36 topics and compare sentiment, topic distributions, and engagement across platforms. Overall, 43.98% of posts are positive, 40.16% neutral, and 15.86% negative. Sentiment varies by topic rather than by platform: corporate and AI-potential themes are mostly positive, while resource-cost topics carry the highest negative sentiment. Topic distributions differ significantly across platforms: LinkedIn focuses on corporate sustainability, X on hardware and cryptocurrency, and Bluesky on environmental resource costs. Niche topics consistently attract more engagement than high-volume ones. These findings have implications for how organizations and policymakers communicate about AI-sustainability, as topic framing and platform audience composition shape public discourse more than platform selection alone. We release the annotated dataset to support future research.
%U https://aclanthology.org/2026.findings-aacl.152/
%P 3004-3018
Markdown (Informal)
[AI-Sustainability Discourse Across Social Media: Sentiment, Topics, and Engagement on LinkedIn, X, and Bluesky](https://aclanthology.org/2026.findings-aacl.152/) (Aldous et al., Findings 2026)
ACL
- Kholoud Khalil Aldous, Md. Rafiul Biswas, Mabrouka Bessghaier, Shimaa Ibrahim, Kais Attia, and Wajdi Zaghouani. 2026. AI-Sustainability Discourse Across Social Media: Sentiment, Topics, and Engagement on LinkedIn, X, and Bluesky. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 3004–3018, Hengqin, China. Association for Computational Linguistics.