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Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks

BACKGROUND: After the Fukushima Daiichi nuclear accident on March 11, 2011, interest in, and fear of, radiation increased among citizens. When such accidents occur, appropriate risk communication must provided by the government. It is therefore necessary to understand the fears of citizens in the da...

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Autores principales: Yagahara, Ayako, Hanai, Keiri, Hasegawa, Shin, Ogasawara, Katsuhiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876491/
https://www.ncbi.nlm.nih.gov/pubmed/29549069
http://dx.doi.org/10.2196/publichealth.7598
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author Yagahara, Ayako
Hanai, Keiri
Hasegawa, Shin
Ogasawara, Katsuhiko
author_facet Yagahara, Ayako
Hanai, Keiri
Hasegawa, Shin
Ogasawara, Katsuhiko
author_sort Yagahara, Ayako
collection PubMed
description BACKGROUND: After the Fukushima Daiichi nuclear accident on March 11, 2011, interest in, and fear of, radiation increased among citizens. When such accidents occur, appropriate risk communication must provided by the government. It is therefore necessary to understand the fears of citizens in the days after such accidents. OBJECTIVE: This study aimed to identify the progression of people’s concerns, specifically fear, from a study of radiation-related tweets in the days after the Fukushima Daiichi nuclear accident. METHODS: From approximately 1.5 million tweets in Japanese including any of the phrases “radiation” (放射線), “radioactivity” (放射能), and “radioactive substance” (放射性物質) sent March 11-17, 2011, we extracted tweets that expressed fear. We then performed a morphological analysis on the extracted tweets. Citizens’ fears were visualized by creating co-occurrence networks using co-occurrence degrees showing relationship strength. Moreover, we calculated the Jaccard coefficient, which is one of the co-occurrence indices for expressing the strength of the relationship between morphemes when creating networks. RESULTS: From the visualization of the co-occurrence networks, we found high citizen interest in “nuclear power plant” on March 11 and 12, “health” on March 12 and 13, “medium” on March 13 and 14, and “economy” on March 15. On March 16 and 17, citizens’ interest changed to “lack of goods in the afflicted area.” In each co-occurrence network, trending topics, citizens’ fears, and opinions to the government were extracted. CONCLUSIONS: This study used Twitter to understand changes in the concerns of Japanese citizens during the week after the Fukushima Daiichi nuclear accident, with a focus specifically on citizens’ fears. We found that immediately after the accident, the interest in the accident itself was high, and then interest shifted to concerns affecting life, such as health and economy, as the week progressed. Clarifying citizens’ fears and the dissemination of information through mass media and social media can add to improved risk communication in the future.
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spelling pubmed-58764912018-04-02 Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks Yagahara, Ayako Hanai, Keiri Hasegawa, Shin Ogasawara, Katsuhiko JMIR Public Health Surveill Original Paper BACKGROUND: After the Fukushima Daiichi nuclear accident on March 11, 2011, interest in, and fear of, radiation increased among citizens. When such accidents occur, appropriate risk communication must provided by the government. It is therefore necessary to understand the fears of citizens in the days after such accidents. OBJECTIVE: This study aimed to identify the progression of people’s concerns, specifically fear, from a study of radiation-related tweets in the days after the Fukushima Daiichi nuclear accident. METHODS: From approximately 1.5 million tweets in Japanese including any of the phrases “radiation” (放射線), “radioactivity” (放射能), and “radioactive substance” (放射性物質) sent March 11-17, 2011, we extracted tweets that expressed fear. We then performed a morphological analysis on the extracted tweets. Citizens’ fears were visualized by creating co-occurrence networks using co-occurrence degrees showing relationship strength. Moreover, we calculated the Jaccard coefficient, which is one of the co-occurrence indices for expressing the strength of the relationship between morphemes when creating networks. RESULTS: From the visualization of the co-occurrence networks, we found high citizen interest in “nuclear power plant” on March 11 and 12, “health” on March 12 and 13, “medium” on March 13 and 14, and “economy” on March 15. On March 16 and 17, citizens’ interest changed to “lack of goods in the afflicted area.” In each co-occurrence network, trending topics, citizens’ fears, and opinions to the government were extracted. CONCLUSIONS: This study used Twitter to understand changes in the concerns of Japanese citizens during the week after the Fukushima Daiichi nuclear accident, with a focus specifically on citizens’ fears. We found that immediately after the accident, the interest in the accident itself was high, and then interest shifted to concerns affecting life, such as health and economy, as the week progressed. Clarifying citizens’ fears and the dissemination of information through mass media and social media can add to improved risk communication in the future. JMIR Publications 2018-03-15 /pmc/articles/PMC5876491/ /pubmed/29549069 http://dx.doi.org/10.2196/publichealth.7598 Text en ©Ayako Yagahara, Keiri Hanai, Shin Hasegawa, Katsuhiko Ogasawara. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 15.03.2018. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Yagahara, Ayako
Hanai, Keiri
Hasegawa, Shin
Ogasawara, Katsuhiko
Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title_full Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title_fullStr Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title_full_unstemmed Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title_short Relationships Among Tweets Related to Radiation: Visualization Using Co-Occurring Networks
title_sort relationships among tweets related to radiation: visualization using co-occurring networks
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876491/
https://www.ncbi.nlm.nih.gov/pubmed/29549069
http://dx.doi.org/10.2196/publichealth.7598
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