@inproceedings{gueta-etal-2025-llms,
title = "Can {LLM}s Learn Macroeconomic Narratives from Social Media?",
author = "Gueta, Almog and
Feder, Amir and
Gekhman, Zorik and
Goldstein, Ariel and
Reichart, Roi",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.4/",
doi = "10.18653/v1/2025.findings-naacl.4",
pages = "57--78",
ISBN = "979-8-89176-195-7",
abstract = "This study empirically tests the $Narrative Economics$ hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts from X (formerly Twitter) which capture economy-related narratives (Data will be shared upon paper acceptance). Employing Natural Language Processing (NLP) methods, we extract and summarize narratives from the tweets. We test their predictive power for $macroeconomic$ forecasting by incorporating the tweets' or the extracted narratives' representations in downstream financial prediction tasks. Our work highlights the challenges in improving macroeconomic models with narrative data, paving the way for the research community to realistically address this important challenge. From a scientific perspective, our investigation offers valuable insights and NLP tools for narrative extraction and summarization using Large Language Models (LLMs), contributing to future research on the role of narratives in economics."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gueta-etal-2025-llms">
<titleInfo>
<title>Can LLMs Learn Macroeconomic Narratives from Social Media?</title>
</titleInfo>
<name type="personal">
<namePart type="given">Almog</namePart>
<namePart type="family">Gueta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amir</namePart>
<namePart type="family">Feder</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zorik</namePart>
<namePart type="family">Gekhman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ariel</namePart>
<namePart type="family">Goldstein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Roi</namePart>
<namePart type="family">Reichart</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: NAACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-195-7</identifier>
</relatedItem>
<abstract>This study empirically tests the Narrative Economics hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts from X (formerly Twitter) which capture economy-related narratives (Data will be shared upon paper acceptance). Employing Natural Language Processing (NLP) methods, we extract and summarize narratives from the tweets. We test their predictive power for macroeconomic forecasting by incorporating the tweets’ or the extracted narratives’ representations in downstream financial prediction tasks. Our work highlights the challenges in improving macroeconomic models with narrative data, paving the way for the research community to realistically address this important challenge. From a scientific perspective, our investigation offers valuable insights and NLP tools for narrative extraction and summarization using Large Language Models (LLMs), contributing to future research on the role of narratives in economics.</abstract>
<identifier type="citekey">gueta-etal-2025-llms</identifier>
<identifier type="doi">10.18653/v1/2025.findings-naacl.4</identifier>
<location>
<url>https://aclanthology.org/2025.findings-naacl.4/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>57</start>
<end>78</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Can LLMs Learn Macroeconomic Narratives from Social Media?
%A Gueta, Almog
%A Feder, Amir
%A Gekhman, Zorik
%A Goldstein, Ariel
%A Reichart, Roi
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F gueta-etal-2025-llms
%X This study empirically tests the Narrative Economics hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts from X (formerly Twitter) which capture economy-related narratives (Data will be shared upon paper acceptance). Employing Natural Language Processing (NLP) methods, we extract and summarize narratives from the tweets. We test their predictive power for macroeconomic forecasting by incorporating the tweets’ or the extracted narratives’ representations in downstream financial prediction tasks. Our work highlights the challenges in improving macroeconomic models with narrative data, paving the way for the research community to realistically address this important challenge. From a scientific perspective, our investigation offers valuable insights and NLP tools for narrative extraction and summarization using Large Language Models (LLMs), contributing to future research on the role of narratives in economics.
%R 10.18653/v1/2025.findings-naacl.4
%U https://aclanthology.org/2025.findings-naacl.4/
%U https://doi.org/10.18653/v1/2025.findings-naacl.4
%P 57-78
Markdown (Informal)
[Can LLMs Learn Macroeconomic Narratives from Social Media?](https://aclanthology.org/2025.findings-naacl.4/) (Gueta et al., Findings 2025)
ACL
- Almog Gueta, Amir Feder, Zorik Gekhman, Ariel Goldstein, and Roi Reichart. 2025. Can LLMs Learn Macroeconomic Narratives from Social Media?. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 57–78, Albuquerque, New Mexico. Association for Computational Linguistics.