Applying machine learning to media analysis improves our understanding of forest conflicts

Conflicts over the management and governance of forests seem to be increasing. Previous media studies in this area have largely focused on analysing the portrayal of specific conflicts. This study aims to review how a broad range of forest conflicts are portrayed in the Swedish media, analysing thei...

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Bibliographic Details
Main Author: Hallberg-Sramek, Isabella
Format: Article in Journal/Newspaper
Language:English
Published: ELSEVIER SCI LTD 2024
Subjects:
Online Access:https://pub.epsilon.slu.se/34740/
https://pub.epsilon.slu.se/34740/1/hallberg-sramek-i-et-al-20240815.pdf
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spelling ftslunivuppsala:oai:pub.epsilon.slu.se:34740 2024-09-15T18:32:15+00:00 Applying machine learning to media analysis improves our understanding of forest conflicts Hallberg-Sramek, Isabella 2024 application/pdf https://pub.epsilon.slu.se/34740/ https://pub.epsilon.slu.se/34740/1/hallberg-sramek-i-et-al-20240815.pdf en eng eng ELSEVIER SCI LTD https://pub.epsilon.slu.se/34740/1/hallberg-sramek-i-et-al-20240815.pdf Hallberg-Sramek, Isabella (2024). Applying machine learning to media analysis improves our understanding of forest conflicts. Land Use Policy. 144 , 107254 [Research article] Forest Science Media Studies Research article NonPeerReviewed 2024 ftslunivuppsala 2024-08-20T23:37:30Z Conflicts over the management and governance of forests seem to be increasing. Previous media studies in this area have largely focused on analysing the portrayal of specific conflicts. This study aims to review how a broad range of forest conflicts are portrayed in the Swedish media, analysing their temporal, spatial, and relational dimensions. We applied topic modelling, a machine learning approach, to analyse 53,600 articles published in the Swedish daily press between 2012 and 2022. We identified 916 topics, of which 94 were of interest for this study. Our results showed ten areas of forest conflicts: hunting and fishing (35 % of total coverage), energy (24 %), recreation and tourism (11 %), nature conservation (8 %), forest damages (6 %), international issues (5 %), forestry (5 %), reindeer husbandry (4 %), media and politics (2 %), and mining (1 %). The overall coverage of forest conflicts increased significantly over the study period, potentially reflecting an actual increase in forest conflicts. Some of the conflicts were continuously reported upon over time, while the coverage of others exhibited seasonal or event -related patterns. Four conflicts received most of their coverage in specific regions, while others were covered across the whole of Sweden. A relational analysis of the conflicts revealed three clusters of forest conflicts focused respectively on industrial, cultural, and conservation conflicts. Our results emphasise the value of using topic modelling to understand the overall patterns and trends of the media coverage of current land use conflicts, while also highlighting potential areas of emerging conflicts that may be of special interest for planners and policy -makers to monitor and manage. Article in Journal/Newspaper reindeer husbandry Swedish University of Agricultural Sciences (SLU): Epsilon Open Archive
institution Open Polar
collection Swedish University of Agricultural Sciences (SLU): Epsilon Open Archive
op_collection_id ftslunivuppsala
language English
topic Forest Science
Media Studies
spellingShingle Forest Science
Media Studies
Hallberg-Sramek, Isabella
Applying machine learning to media analysis improves our understanding of forest conflicts
topic_facet Forest Science
Media Studies
description Conflicts over the management and governance of forests seem to be increasing. Previous media studies in this area have largely focused on analysing the portrayal of specific conflicts. This study aims to review how a broad range of forest conflicts are portrayed in the Swedish media, analysing their temporal, spatial, and relational dimensions. We applied topic modelling, a machine learning approach, to analyse 53,600 articles published in the Swedish daily press between 2012 and 2022. We identified 916 topics, of which 94 were of interest for this study. Our results showed ten areas of forest conflicts: hunting and fishing (35 % of total coverage), energy (24 %), recreation and tourism (11 %), nature conservation (8 %), forest damages (6 %), international issues (5 %), forestry (5 %), reindeer husbandry (4 %), media and politics (2 %), and mining (1 %). The overall coverage of forest conflicts increased significantly over the study period, potentially reflecting an actual increase in forest conflicts. Some of the conflicts were continuously reported upon over time, while the coverage of others exhibited seasonal or event -related patterns. Four conflicts received most of their coverage in specific regions, while others were covered across the whole of Sweden. A relational analysis of the conflicts revealed three clusters of forest conflicts focused respectively on industrial, cultural, and conservation conflicts. Our results emphasise the value of using topic modelling to understand the overall patterns and trends of the media coverage of current land use conflicts, while also highlighting potential areas of emerging conflicts that may be of special interest for planners and policy -makers to monitor and manage.
format Article in Journal/Newspaper
author Hallberg-Sramek, Isabella
author_facet Hallberg-Sramek, Isabella
author_sort Hallberg-Sramek, Isabella
title Applying machine learning to media analysis improves our understanding of forest conflicts
title_short Applying machine learning to media analysis improves our understanding of forest conflicts
title_full Applying machine learning to media analysis improves our understanding of forest conflicts
title_fullStr Applying machine learning to media analysis improves our understanding of forest conflicts
title_full_unstemmed Applying machine learning to media analysis improves our understanding of forest conflicts
title_sort applying machine learning to media analysis improves our understanding of forest conflicts
publisher ELSEVIER SCI LTD
publishDate 2024
url https://pub.epsilon.slu.se/34740/
https://pub.epsilon.slu.se/34740/1/hallberg-sramek-i-et-al-20240815.pdf
genre reindeer husbandry
genre_facet reindeer husbandry
op_relation https://pub.epsilon.slu.se/34740/1/hallberg-sramek-i-et-al-20240815.pdf
Hallberg-Sramek, Isabella (2024). Applying machine learning to media analysis improves our understanding of forest conflicts. Land Use Policy. 144 , 107254 [Research article]
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