@inproceedings{ranjit-etal-2024-oath,
title = "{OATH}-Frames: Characterizing Online Attitudes Towards Homelessness with {LLM} Assistants",
author = "Ranjit, Jaspreet and
Joshi, Brihi and
Dorn, Rebecca and
Petry, Laura and
Koumoundouros, Olga and
Bottarini, Jayne and
Liu, Peichen and
Rice, Eric and
Swayamdipta, Swabha",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.724",
doi = "10.18653/v1/2024.emnlp-main.724",
pages = "13033--13059",
abstract = "Warning: Contents of this paper may be upsetting.Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5{\mbox{$\times$}} speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.",
}
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<abstract>Warning: Contents of this paper may be upsetting.Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5\times speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.</abstract>
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%0 Conference Proceedings
%T OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants
%A Ranjit, Jaspreet
%A Joshi, Brihi
%A Dorn, Rebecca
%A Petry, Laura
%A Koumoundouros, Olga
%A Bottarini, Jayne
%A Liu, Peichen
%A Rice, Eric
%A Swayamdipta, Swabha
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ranjit-etal-2024-oath
%X Warning: Contents of this paper may be upsetting.Public attitudes towards key societal issues, expressed on online media, are of immense value in policy and reform efforts, yet challenging to understand at scale. We study one such social issue: homelessness in the U.S., by leveraging the remarkable capabilities of large language models to assist social work experts in analyzing millions of posts from Twitter. We introduce a framing typology: Online Attitudes Towards Homelessness (OATH) Frames: nine hierarchical frames capturing critiques, responses and perceptions. We release annotations with varying degrees of assistance from language models, with immense benefits in scaling: 6.5\times speedup in annotation time while only incurring a 3 point F1 reduction in performance with respect to the domain experts. Our experiments demonstrate the value of modeling OATH-Frames over existing sentiment and toxicity classifiers. Our large-scale analysis with predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in attitudes across states, time periods and vulnerable populations, enabling new insights on the issue. Our work provides a general framework to understand nuanced public attitudes at scale, on issues beyond homelessness.
%R 10.18653/v1/2024.emnlp-main.724
%U https://aclanthology.org/2024.emnlp-main.724
%U https://doi.org/10.18653/v1/2024.emnlp-main.724
%P 13033-13059
Markdown (Informal)
[OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants](https://aclanthology.org/2024.emnlp-main.724) (Ranjit et al., EMNLP 2024)
ACL
- Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn, Laura Petry, Olga Koumoundouros, Jayne Bottarini, Peichen Liu, Eric Rice, and Swabha Swayamdipta. 2024. OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13033–13059, Miami, Florida, USA. Association for Computational Linguistics.