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

Abstract

BackgroundAfter 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.ObjectiveThis 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.MethodsFrom 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.ResultsFrom 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.ConclusionsThis 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.

Nuclear energy: Twitter data mining for social listening analysis

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Contextualising the 2019 E-Cigarette Health Scare: Insights from Twitter

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Changing Emotions About Fukushima Related to the Fukushima Nuclear Power Station Accident—How Rumors Determined People’s Attitudes: Social Media Sentiment Analysis

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