Harnessing deep learning to monitor people's perceptions towards climate change on social media

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F25%3A00646899" target="_blank" >RIV/60077344:_____/25:00646899 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1038/s41598-025-97441-1" target="_blank" >https://doi.org/10.1038/s41598-025-97441-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-97441-1" target="_blank" >10.1038/s41598-025-97441-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Harnessing deep learning to monitor people's perceptions towards climate change on social media

Popis výsledku v původním jazyce

Social media has become a popular stage for people's views over climate change. Monitoring how climate change is perceived on social media is relevant for informed decision-making. This work advances the way social media users' perceptions and reactions towards climate change can be understood over time, by implementing a scalable methodological framework grounded on natural language processing. The framework was tested in over 1771 thousand X/Twitter posts of Spanish, Portuguese, and English discourses from Southwestern Europe. The employed models were successful (i.e., > 84% success rate) in detecting relevant climate change posts. The methodology detected specific climate phenomena in users' discourse, coinciding with the occurrence of major climatic events in the test area (e.g., wildfires, storms). The classification of sentiments, emotions, and irony was also efficient, with evaluation metrics ranging from 71 to 92%. Most users' reactions were neutral (> 35%) or negative (> 39%), mostly associated to sentiments of anger and sadness over climate impacts. Almost a quarter of posts showed ironic content, reflecting the common use of irony in social media communication. Our exploratory study holds potential to support climate decisions based on deep learning tools from monitoring people's perceptions towards climate issues in the online space.

Název v anglickém jazyce

Harnessing deep learning to monitor people's perceptions towards climate change on social media

Popis výsledku anglicky

Social media has become a popular stage for people's views over climate change. Monitoring how climate change is perceived on social media is relevant for informed decision-making. This work advances the way social media users' perceptions and reactions towards climate change can be understood over time, by implementing a scalable methodological framework grounded on natural language processing. The framework was tested in over 1771 thousand X/Twitter posts of Spanish, Portuguese, and English discourses from Southwestern Europe. The employed models were successful (i.e., > 84% success rate) in detecting relevant climate change posts. The methodology detected specific climate phenomena in users' discourse, coinciding with the occurrence of major climatic events in the test area (e.g., wildfires, storms). The classification of sentiments, emotions, and irony was also efficient, with evaluation metrics ranging from 71 to 92%. Most users' reactions were neutral (> 35%) or negative (> 39%), mostly associated to sentiments of anger and sadness over climate impacts. Almost a quarter of posts showed ironic content, reflecting the common use of irony in social media communication. Our exploratory study holds potential to support climate decisions based on deep learning tools from monitoring people's perceptions towards climate issues in the online space.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50802 - Media and socio-cultural communication

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

14924

Kód UT WoS článku

001478481100019

EID výsledku v databázi Scopus

2-s2.0-105003819873