@inproceedings{shokri-etal-2024-subjectivity,
title = "Subjectivity Detection in {E}nglish News using Large Language Models",
author = "Shokri, Mohammad and
Sharma, Vivek and
Filatova, Elena and
Jain, Shweta and
Levitan, Sarah",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.17",
pages = "215--226",
abstract = "Trust in media has reached a historical low as consumers increasingly doubt the credibility of the news they encounter. This growing skepticism is exacerbated by the prevalence of opinion-driven articles, which can influence readers{'} beliefs to align with the authors{'} viewpoints. In response to this trend, this study examines the expression of opinions in news by detecting subjective and objective language. We conduct an analysis of the subjectivity present in various news datasets and evaluate how different language models detect subjectivity and generalize to out-of-distribution data. We also investigate the use of in-context learning (ICL) within large language models (LLMs) and propose a straightforward prompting method that outperforms standard ICL and chain-of-thought (CoT) prompts.",
}
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<abstract>Trust in media has reached a historical low as consumers increasingly doubt the credibility of the news they encounter. This growing skepticism is exacerbated by the prevalence of opinion-driven articles, which can influence readers’ beliefs to align with the authors’ viewpoints. In response to this trend, this study examines the expression of opinions in news by detecting subjective and objective language. We conduct an analysis of the subjectivity present in various news datasets and evaluate how different language models detect subjectivity and generalize to out-of-distribution data. We also investigate the use of in-context learning (ICL) within large language models (LLMs) and propose a straightforward prompting method that outperforms standard ICL and chain-of-thought (CoT) prompts.</abstract>
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%0 Conference Proceedings
%T Subjectivity Detection in English News using Large Language Models
%A Shokri, Mohammad
%A Sharma, Vivek
%A Filatova, Elena
%A Jain, Shweta
%A Levitan, Sarah
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F shokri-etal-2024-subjectivity
%X Trust in media has reached a historical low as consumers increasingly doubt the credibility of the news they encounter. This growing skepticism is exacerbated by the prevalence of opinion-driven articles, which can influence readers’ beliefs to align with the authors’ viewpoints. In response to this trend, this study examines the expression of opinions in news by detecting subjective and objective language. We conduct an analysis of the subjectivity present in various news datasets and evaluate how different language models detect subjectivity and generalize to out-of-distribution data. We also investigate the use of in-context learning (ICL) within large language models (LLMs) and propose a straightforward prompting method that outperforms standard ICL and chain-of-thought (CoT) prompts.
%U https://aclanthology.org/2024.wassa-1.17
%P 215-226
Markdown (Informal)
[Subjectivity Detection in English News using Large Language Models](https://aclanthology.org/2024.wassa-1.17) (Shokri et al., WASSA-WS 2024)
ACL
- Mohammad Shokri, Vivek Sharma, Elena Filatova, Shweta Jain, and Sarah Levitan. 2024. Subjectivity Detection in English News using Large Language Models. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 215–226, Bangkok, Thailand. Association for Computational Linguistics.