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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Mar 11, 2020
Date Accepted: Jun 11, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Hasegawa S, Suzuki T, Yagahara A, Kanda R, Aono T, Yajima K, Ogasawara K

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

J Med Internet Res 2020;22(9):e18662

DOI: 10.2196/18662

PMID: 32876574

PMCID: 7495261

Emotional transition to Fukushima relating Fukushima Nuclear Power Station Accident – How rumors made people’s attitude: Sentiment Analysis

  • Shin Hasegawa; 
  • Teppei Suzuki; 
  • Ayako Yagahara; 
  • Reiko Kanda; 
  • Tatsuo Aono; 
  • Kazuaki Yajima; 
  • Katsuhiko Ogasawara

ABSTRACT

Background:

Public interest in radiation rose after the TEPCO Fukushima Daiichi Nuclear Power Station accident was caused by an earthquake off the Pacific coast of Tohoku on March 11, 2011. Various reports on the accident and radiation were spread by the mass media, and people displayed their emotional reactions on Twitter, Facebook, and other social networking sites, which were thought to be related to information about the Fukushima accident, and fears about radiation were spread, leading to harmful rumors about Fukushima and the refusal to test children for radiation. It is believed that identifying the process by which people emotionally responded to this information and hence became gripped by an increased aversion to Fukushima might be useful in risk communication when similar disasters and accidents occur in future. There are few studies surveying how people feel about radiation in Fukushima and other regions in an unbiased form.

Objective:

The purpose of this study is to identify how the feelings of local residents toward radiation have changed according to Twitter.

Methods:

We used approximately 19 million tweets in Japanese containing the words “radiation” (放射線), “radioactivity” (放射能), and “radioactive substances” (放射性物質) that were posted to Twitter over a one-year period following the Fukushima nuclear accident. We used regional identifiers contained in tweets (nouns, proper nouns, place names, postal codes, and telephone numbers) to categorize them according to their prefecture, and then analyzed the feelings toward those prefectures from the semantic orientation of the words contained in individual tweets (positive impressions or negative impressions).

Results:

Overall, tweets about radiation have decreased, and feelings about radiation have positively trended. We determined that on average, tweets associating Fukushima Prefecture with radiation show more positive feelings than those about other prefectures, but have negatively trended over time. We also found that as other tweets have positively trended, only bots and retweets about Fukushima Prefecture have negatively trended.

Conclusions:

The number of tweets about radiation has decreased overall, and feelings about radiation have positively trended . However, the fact that tweets about Fukushima Prefecture negatively trended, despite decreasing in percentage, which suggests that negative feelings toward Fukushima Prefecture have become more extreme. We found that while the bots and retweets that were not about Fukushima Prefecture gradually trended toward positive feelings, the bots and retweets about Fukushima Prefecture trended toward negative feelings. These results strongly suggest that an aversion toward Fukushima has increased as a result of the negative feelings toward Fukushima Prefecture concerning radiation as spread by bots and retweets.


 Citation

Please cite as:

Hasegawa S, Suzuki T, Yagahara A, Kanda R, Aono T, Yajima K, Ogasawara K

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

J Med Internet Res 2020;22(9):e18662

DOI: 10.2196/18662

PMID: 32876574

PMCID: 7495261

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