Publication: Encoding Social Media Wording Indexes to Analyze PM2.5 Problem Perception
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Issued Date
2023
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application/pdf
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มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
Springer Geography. Vol , No. (2023), p.101-111
Suggested Citation
Nipithwittaya S. Encoding Social Media Wording Indexes to Analyze PM2.5 Problem Perception. Springer Geography. Vol , No. (2023), p.101-111. doi:10.1007/978-3-031-16217-6_7 Retrieved from: https://hdl.handle.net/20.500.14740/11258
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Abstract
The purpose of this research was to create a database of areas with PM2.5 and to map the spatial and temporal PM2.5 density from a social media wording index. In addition, the study intends to determine the perception of PM2.5 through text from social media so that people can find ways to protect themselves from the impact of PM2.5. The information acquired is from image data and location data in eight provinces of the upper northern region of Thailand—namely, Mae Hong Son, Chiang Mai, Chiang Rai, Phayao, Nan, Lampang, Phrae and Lamphun. According to the search, the terms define the repetition-frequency index for data analysis. It can be used to analyze and map the PM2.5 density and can show the spatial PM2.5 in the study area. Moreover, the results of the study reflect that people are alert and aware of PM2.5 problems through social media text. This includes the views and opinions of the people about the designated sources and effects of PM2.5 that the first comes from open burning. It is comprised of the impact that occurs with both the visibility of the vision’s impact on quality of life, health and environment. People are aware of the problems and the consequences that have arisen and have experienced such problems. Guidelines for self-solution of problems are things such as using a PM2.5 mask and going into the open air during the PM2.5 crisis. Moreover, Chiang Mai and Chiang Rai provinces are where individuals suffer from PM2.5 problems and are the most discussed on social media, respectively. This corresponds to air-quality standard reports from the local stations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.