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http://hdl.handle.net/20.500.12188/26405
Title: | Western Balkan societies’ awareness of air pollution. Estimations using natural language processing techniques | Authors: | Madjar, Angela Gjorshoska, Ivana Prodanova, Jana Dedinec, Aleksandra Kocarev, Ljupco |
Keywords: | Air pollution, Western Balkans, Twitter, Sentiment analysis, Topic modelling, Cross-correlation | Issue Date: | 2023 | Journal: | Ecological Informatics | Abstract: | Air pollution remains a severe concern in European countries, especially in Western Balkan, where the air monitoring data point to harmful ambient pollution. The public concern with this issue becomes particularly critical during the fall and winter months, when the contamination is more visible, provoking a series of reactions directed principally to the government authorities as the responsible entities for regulating air pollution levels. Since citizen-contributed data are generally considered valuable additional information for assessing the impacts of air pollution, the public contribution could act as a tool for increasing awareness and response about air pollution. Consequently, this study’s objective focuses on researching public awareness of air pollution in Western Balkan. The study assumes that citizens’ reactions will grow more intensely during the months with an increase in air pollution levels, principally due to winter heating. Therefore, Twitter activity and news articles related to air pollution have been investigated for the case of Macedonia, Serbia, Bosnia and Herzegovina and Montenegro, from November 2021 to March 2022. Natural Language Processing techniques such as sentiment analysis, topic modelling, and cross-correlations statistical analysis were employed to determine the relationship between Twitter discussions and news with actual PM10 levels measured by official air monitoring stations. The aim was to observe whether tweets and news teasers reflect the realistic air pollution situation. The results affirm that social media discussions, mainly with a negative connotation, can serve as a measure of public awareness of temporal changes in the PM10 concentration in the air and the negative consequences. The content of the resources reveals several topics of concern, contributing to better identification of public opinion and possibilities for tracking news trends. Nevertheless, attention should be paid to news interpretation, provided that sometimes they might offer a more neutral understanding of the situation, failing, in this way, to present the actual air conditions and possibly impacting society in forming an unrealistic opinion. Additionally, the public might not be able to obtain sufficient or accurate information about the primary sources of air pollution, emphasizing the need for more transparent communication and greater education regarding air pollution monitoring. Finally, the study provides deeper insights into the content of the data and helps detect the reasons for skepticism towards proenvironmental behavior occurring in social media discussions. Explicitly, personal disappointment with the air quality should be taken as an inflection point by responsible parties to intervene in improving citizens’ quality of life. | URI: | http://hdl.handle.net/20.500.12188/26405 |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
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