MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning
This study introduces MediaWatchers4Climate, a methodological framework that leverages machine learning to evaluate the accuracy and rhetorical framing of climate change narratives in Greek online media. The model is designed to analyze large-scale textual data from over 1500 certified digital outle...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/7/2/53 |
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| author | Thomai Baltzi Stella Nikitaki Fani Galatsopoulou Ioanna Kostarella Andreas Veglis Vasilis Vasilopoulos Dimitris Papaevagelou Antonis Skamnakis |
| author_facet | Thomai Baltzi Stella Nikitaki Fani Galatsopoulou Ioanna Kostarella Andreas Veglis Vasilis Vasilopoulos Dimitris Papaevagelou Antonis Skamnakis |
| author_sort | Thomai Baltzi |
| collection | DOAJ |
| description | This study introduces MediaWatchers4Climate, a methodological framework that leverages machine learning to evaluate the accuracy and rhetorical framing of climate change narratives in Greek online media. The model is designed to analyze large-scale textual data from over 1500 certified digital outlets registered in the Greek Online Media Registry. Through keyword-based filtering, thematic clustering, and content comparison techniques, the framework aims to detect discursive shifts, trace the replication of news stories, and identify misinformation patterns. While the current phase focuses on model development and data structuring, preliminary observations suggest significant content repetition across sources and a lack of original reporting on climate issues. The project ultimately seeks to promote evidence-based reasoning and enhance public resilience to misinformation related to the climate crisis. |
| format | Article |
| id | doaj-art-e1c4c562517342dcb4a33123735b118a |
| institution | Kabale University |
| issn | 2504-4990 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-e1c4c562517342dcb4a33123735b118a2025-08-20T03:27:28ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902025-06-01725310.3390/make7020053MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine LearningThomai Baltzi0Stella Nikitaki1Fani Galatsopoulou2Ioanna Kostarella3Andreas Veglis4Vasilis Vasilopoulos5Dimitris Papaevagelou6Antonis Skamnakis7School of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceCivic Information Office, Nikosthenous Str., 11635 Athens, GreeceSchool of Journalism and Mass Communications, Aristotle University of Thessaloniki, 54632 Thessaloniki, GreeceThis study introduces MediaWatchers4Climate, a methodological framework that leverages machine learning to evaluate the accuracy and rhetorical framing of climate change narratives in Greek online media. The model is designed to analyze large-scale textual data from over 1500 certified digital outlets registered in the Greek Online Media Registry. Through keyword-based filtering, thematic clustering, and content comparison techniques, the framework aims to detect discursive shifts, trace the replication of news stories, and identify misinformation patterns. While the current phase focuses on model development and data structuring, preliminary observations suggest significant content repetition across sources and a lack of original reporting on climate issues. The project ultimately seeks to promote evidence-based reasoning and enhance public resilience to misinformation related to the climate crisis.https://www.mdpi.com/2504-4990/7/2/53climate changemedia literacymachine learningGreek media |
| spellingShingle | Thomai Baltzi Stella Nikitaki Fani Galatsopoulou Ioanna Kostarella Andreas Veglis Vasilis Vasilopoulos Dimitris Papaevagelou Antonis Skamnakis MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning Machine Learning and Knowledge Extraction climate change media literacy machine learning Greek media |
| title | MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning |
| title_full | MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning |
| title_fullStr | MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning |
| title_full_unstemmed | MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning |
| title_short | MediaWatchers4Climate: Assessing the Accuracy of Climate Change Narratives in Greek Media Through Machine Learning |
| title_sort | mediawatchers4climate assessing the accuracy of climate change narratives in greek media through machine learning |
| topic | climate change media literacy machine learning Greek media |
| url | https://www.mdpi.com/2504-4990/7/2/53 |
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