关键词
推特，交流，科学，传播，公众，参与，计算分析
摘要
自20世纪中叶以来，人们对科学的社会性认知得到了广泛的研究。该项目旨在研究数字生活框架内的科学与公众互动问题，以完善有关科学对社会的影响的经典研究，尤其是在推特社交网络上。因此，本研究提出了一种低成本，便于研究人员复制的方法，设计一种可以在代表性推文集合上运行的算法，使用数据挖掘和自然语言处理的计算技术对推文的内容进行分析。为了测试该工具，我们分析了科普达人Neil DeGrasse Tyson的推文，并根据以下方面计算期信息内容的影响力：1）点赞和转发； 2）流行度和争议内容程度的建议测量； 3）语义网络。在按照“科学”，“文化”，“政治-社会”，“信仰”，“媒体”和“情感”类别对其话语的相关元素进行识别和分类后，结果表明，一个传播者满怀激情地传递信息的过程可以唤醒公众更深刻的回应，这一点同样适用于社会政治问题。此外，我们还发现与单单讨论科学的核心概念相比，科学讨论之外的许多概念会引起更大的兴趣。两项结果都表明，当与其他方面联系起来时，科学会受到更大的关注。
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技术信息
收到: 26-02-2020
修订: 20-03-2020
公认: 27-04-2020
OnlineFirst: 15-06-2020
发布日期: 01-10-2020
文章修改时间: 23 天 | 期刊编号的平均时间修订 65: 36 天
文章接受时间: 61 天 | 期刊编号的平均接受时间 65: 79 天
预印本编辑时间: 173 天 | 期刊编号的平均编辑时间预印 65: 191 天
文章编辑时间: 218 天 | 期刊编号的平均编辑时间 65: 236 天
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被引用
引述 Web of Science
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