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        <title>Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning</title>
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    <abstract>Frames of Communication (FoCs) are ubiquitous in social media discourse. They define what counts as a problem, diagnose what is causing the problem, elicit moral judgments and imply remedies for resolving the problem. Most research on automatic frame detection involved the recognition of the problems addressed by frames, but did not consider the articulation of frames. Articulating an FoC involves reasoning with salient problems, their cause and eventual solution. In this paper we present a method for Discovering and Articulating FoCs (DA-FoC) that relies on a combination of Chain-of-Thought prompting of large language models (LLMs) with In-Context Active Curriculum Learning. Very promising evaluation results indicate that 86.72% of the FoCs encoded by communication experts on the same reference dataset were also uncovered by DA-FoC. Moreover, DA-FoC uncovered many new FoCs, which escaped the experts. Interestingly, 55.1% of the known FoCs were judged as being better articulated than the human-written ones, while 93.8% of the new FoCs were judged as having sound rationale and being clearly articulated.</abstract>
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%0 Conference Proceedings
%T Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning
%A Weinzierl, Maxwell
%A Harabagiu, Sanda
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F weinzierl-harabagiu-2024-discovering
%X Frames of Communication (FoCs) are ubiquitous in social media discourse. They define what counts as a problem, diagnose what is causing the problem, elicit moral judgments and imply remedies for resolving the problem. Most research on automatic frame detection involved the recognition of the problems addressed by frames, but did not consider the articulation of frames. Articulating an FoC involves reasoning with salient problems, their cause and eventual solution. In this paper we present a method for Discovering and Articulating FoCs (DA-FoC) that relies on a combination of Chain-of-Thought prompting of large language models (LLMs) with In-Context Active Curriculum Learning. Very promising evaluation results indicate that 86.72% of the FoCs encoded by communication experts on the same reference dataset were also uncovered by DA-FoC. Moreover, DA-FoC uncovered many new FoCs, which escaped the experts. Interestingly, 55.1% of the known FoCs were judged as being better articulated than the human-written ones, while 93.8% of the new FoCs were judged as having sound rationale and being clearly articulated.
%U https://aclanthology.org/2024.eacl-long.97
%P 1617-1631

Markdown (Informal)

[Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning](https://aclanthology.org/2024.eacl-long.97) (Weinzierl & Harabagiu, EACL 2024)

• Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning (Weinzierl & Harabagiu, EACL 2024)

ACL

• Maxwell Weinzierl and Sanda Harabagiu. 2024. Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1617–1631, St. Julian’s, Malta. Association for Computational Linguistics.