Suppr超能文献

与辐射相关推文之间的关系:使用共现网络进行可视化

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

作者信息

Yagahara Ayako, Hanai Keiri, Hasegawa Shin, Ogasawara Katsuhiko

机构信息

Faculty of Health Sciences, Hokkaido University of Science, Sapporo, Japan.

Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.

出版信息

JMIR Public Health Surveill. 2018 Mar 15;4(1):e26. doi: 10.2196/publichealth.7598.

Abstract

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" (), "radioactivity" (), and "radioactive substance" () 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.

摘要

背景

2011年3月11日福岛第一核电站事故发生后,民众对辐射的关注和恐惧增加。此类事故发生时,政府必须提供适当的风险沟通。因此,有必要了解事故发生后数天民众的恐惧心理。

目的

本研究旨在通过对福岛第一核电站事故发生后数天内与辐射相关推文的研究,确定民众担忧情绪,特别是恐惧情绪的发展变化。

方法

从2011年3月11日至17日发送的约150万条包含“辐射”()、“放射性”()和“放射性物质”()等短语的日语推文中,提取表达恐惧的推文。然后对提取的推文进行形态分析。通过使用显示关系强度的共现度创建共现网络,将民众的恐惧可视化。此外,我们计算了杰卡德系数,它是创建网络时用于表达语素之间关系强度的共现指标之一。

结果

从共现网络的可视化结果来看,我们发现3月11日和12日民众对“核电站”高度关注,12日和13日关注“健康”,13日和14日关注“媒体”,15日关注“经济”。3月16日和17日,民众的关注点转向“受灾地区物资短缺”。在每个共现网络中,提取了热门话题、民众的恐惧以及对政府的意见。

结论

本研究利用推特了解福岛第一核电站事故发生后一周内日本民众担忧情绪的变化,特别关注民众的恐惧心理。我们发现事故刚发生后,民众对事故本身关注度很高,随着时间推移,关注点转向影响生活的方面,如健康和经济。明确民众的恐惧并通过大众媒体和社交媒体传播信息,有助于未来改善风险沟通。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4621/5876491/1b51e851f7e9/publichealth_v4i1e26_fig2.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验