Psychological distance and efficacy: analyzing the framing of climate change on US agricultural news websites using LLM-assisted content analysis

Agriculture has significantly contributed to overall greenhouse gas emissions. Farmers’ perceptions of climate change play a critical role in shaping their attitudes toward adopting sustainable agricultural practices. News media play a crucial role in shaping public awareness and perceptions of climate change, which in turn may influence people’s behavior. Agricultural media serve as an important source of information for agricultural practices. However, there is limited research focusing on agricultural media and its potential relation to farmer practices. This study aims to address these gaps by applying a Large Language Model-assisted Content Analysis method to analyze climate change-related articles published between 2014 and 2023, across three US agricultural news websites. The findings indicate that agricultural media frequently use efficacy-related frames to discuss climate change. Rather than engaging in political or scientific debates, these outlets focus on information that is directly applicable and relevant to farming practices. Additionally, agricultural media may mitigate psychological distance in the reader (the farmer) by emphasizing immediate climate change risks, and addressing local concerns.

Authors

Institutions

Publication Details

Journal
Frontiers in Communication
Published
2026-02-04
DOI
https://doi.org/10.3389/fcomm.2026.1759296
Primary Topic
Climate Change Communication and Perception
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Psychological distance and efficacy: analyzing the framing of climate change on US agricultural news websites using LLM-assisted content analysis

W. Li, James L. Shanahan, Yingcui Liu
Frontiers in Communication
Climate Change Communication and Perception
article

Psychological distance and efficacy: analyzing the framing of climate change on US agricultural news websites using LLM-assisted content analysis

W. Li, James L. Shanahan, Yingcui Liu
article en

Abstract

Agriculture has significantly contributed to overall greenhouse gas emissions. Farmers’ perceptions of climate change play a critical role in shaping their attitudes toward adopting sustainable agricultural practices. News media play a crucial role in shaping public awareness and perceptions of climate change, which in turn may influence people’s behavior. Agricultural media serve as an important source of information for agricultural practices. However, there is limited research focusing on agricultural media and its potential relation to farmer practices. This study aims to address these gaps by applying a Large Language Model-assisted Content Analysis method to analyze climate change-related articles published between 2014 and 2023, across three US agricultural news websites. The findings indicate that agricultural media frequently use efficacy-related frames to discuss climate change. Rather than engaging in political or scientific debates, these outlets focus on information that is directly applicable and relevant to farming practices. Additionally, agricultural media may mitigate psychological distance in the reader (the farmer) by emphasizing immediate climate change risks, and addressing local concerns.

Frontiers in CommunicationVol. 11
Danish School of Media and Journalism (DK), Indiana University Bloomington (US)
Indiana University Bloomington
Zero hunger
Openalex Percentile: Top 70%
Climate Change Communication and Perception
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.