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福岛第一核电站事故半年后,通过对有影响力者传播信息的可视化,揭示了推特在科学传播中的应用。

Twitter use in scientific communication revealed by visualization of information spreading by influencers within half a year after the Fukushima Daiichi nuclear power plant accident.

机构信息

Department of Radiation Protection, Soma Central Hospital, Soma, Fukushima, Japan.

Science for Innovation Policy Unit, Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto, Japan.

出版信息

PLoS One. 2018 Sep 7;13(9):e0203594. doi: 10.1371/journal.pone.0203594. eCollection 2018.

DOI:10.1371/journal.pone.0203594
PMID:30192829
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6128581/
Abstract

Scientific communication through social media, particularly Twitter has been gaining importance in recent years. As such, it is critical to understand how information is transmitted and dispersed through outlets such as Twitter, particularly in emergency situations where there is an urgent need to relay scientific information. The purpose of this study is to examine how original tweets and retweets on Twitter were used to diffuse radiation related information after the Fukushima Daiichi nuclear power plant accident. Out of the Twitter database, we purchased all tweets (including replies) and retweets related to Fukushima Daiichi nuclear power plant accident and or radiation sent from March 2nd, 2011 to September 15th, 2011. This time frame represents the first six months after the East Japan earthquake, which occurred on March 11th, 2011. Using the obtained data, we examined the number of tweets and retweets and found that only a small number of Twitter users were the source of the original posts that were retweeted during the study period. We have termed these specific accounts as "influencers". We identified the top 100 influencers and classified the contents of their tweets into 3 groups by analyzing the document vectors of the text. Then, we examined the number of retweets for each of the 3 groups of influencers, and created a retweet network diagram to assess how the contents of their tweets were being spread. The keyword "radiation" was mentioned in over 24 million tweets and retweets during the study period. Retweets accounted for roughly half (49.7%) of this number, and the top 2% of Twitter accounts defined as "influencers" were the source of the original posts that accounted for 80.3% of the total retweets. The majority of the top 100 influencers had individual Twitter accounts bearing real names. While retweets were intensively diffused within a fixed population, especially within the same groups with similar document vectors, a group of influencers accounted for the majority of retweets one month after the disaster, and the share of each group did not change even after proven scientific information became more available.

摘要

科学传播通过社交媒体,特别是 Twitter,近年来变得越来越重要。因此,了解信息是如何通过 Twitter 等渠道传播和扩散的至关重要,特别是在紧急情况下,迫切需要传递科学信息。本研究的目的是研究在福岛第一核电站事故后,Twitter 上的原始推文和转发如何传播与辐射相关的信息。从 Twitter 数据库中,我们购买了 2011 年 3 月 2 日至 9 月 15 日期间与福岛第一核电站事故和/或辐射相关的所有推文(包括回复)和转发。这段时间代表了 2011 年 3 月 11 日东日本地震后的头六个月。利用获得的数据,我们检查了推文和转发的数量,发现只有少数 Twitter 用户是研究期间被转发的原始帖子的来源。我们将这些特定账户称为“影响者”。我们确定了前 100 名影响者,并通过分析文本的文档向量将他们的推文内容分为 3 组。然后,我们检查了这 3 组影响者的每条推文的转发数量,并创建了一个转发网络图,以评估他们的推文内容是如何传播的。在研究期间,“辐射”一词在超过 2400 万条推文和转发中被提及。转发约占这个数字的一半(49.7%),前 2%的被定义为“影响者”的 Twitter 账户是占总转发量 80.3%的原始帖子的来源。前 100 名影响者中的大多数都有真实姓名的个人 Twitter 账户。虽然转发在固定人群中得到了密集的扩散,特别是在具有相似文档向量的相同群组内,但在灾难发生一个月后,一组影响者占据了大部分转发,即使有更多的可靠科学信息可用,每个群组的份额也没有改变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/678199a795da/pone.0203594.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/e8a2fee8ace3/pone.0203594.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/0397fa7e3db9/pone.0203594.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/1b7fe65341da/pone.0203594.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/678199a795da/pone.0203594.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/e8a2fee8ace3/pone.0203594.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/0397fa7e3db9/pone.0203594.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/1b7fe65341da/pone.0203594.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc48/6128581/678199a795da/pone.0203594.g004.jpg

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