Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation

Challenge:	Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups.
Approach:	They analyze social media ads using large language models to examine their performance . they find that LLMs perform well overall, but certain biases exist .
Outcome:	The results show that LLMs perform well overall, but certain biases exist in certain demographic groups.

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Tunazzina Islam

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Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy (2025.findings-naacl)

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Tunazzina Islam, Dan Goldwasser

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Bryan Chen Zhengyu Tan, Roy Ka-Wei Lee

Responsible & Trustworthy NLP

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White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs (2025.acl-long)

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Yixin Wan, Kai-Wei Chang

Responsible & Trustworthy NLP

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Predicting Narratives of Climate Obstruction in Social Media Advertising (2024.findings-acl)

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Harri Rowlands, Gaku Morio, Dylan Tanner, Christopher Manning

Challenge:	Social media advertising allows entities to construct narratives that align with their commercial interests and sway public perception.
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Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong

Responsible & Trustworthy NLP

Challenge:	Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms.
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Towards Fine-grained Classification of Climate Change related Social Media Text (2022.acl-srw)

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Roopal Vaid, Kartikey Pant, Manish Shrivastava

Information Extraction & Text Mining

Challenge:	a new study examines the fine-grained classification and classification of climate change-related social media text.
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Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions (2026.eacl-long)

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Angana Borah, Rada Mihalcea, Veronica Perez-Rosas

Reasoning

Challenge:	Existing challenges in misinformation exposure and susceptibility vary across demographics.
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ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)

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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith

Responsible & Trustworthy NLP

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Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)

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Indira Sen, Marlene Lutz, Elisa Rogers, David Garcia, Markus Strohmaier

Challenge:	211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories.
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Outcome:	211 studies on the representativeness of large language models are reviewed . 29% of the studies report positive conclusions, but 30% fail to specify subcategories . authors recommend more precise evaluation methods and documentation of demographic attributes .