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Examining Characteristics of Traditional and Twitter Citation

机译:检查传统和Twitter引用的特征

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Social media has attracted the attention of the academic community as an emerging communication channel. This channel opens a new opportunity to measure the impact of social use of scholarly publications in social media (altmetrics) which supplements our understanding on the scholarly impact of publications (bibliometrics). Two different channels, social media and journal, are known to establish various citation patterns statistically. However, thematic difference between altmetrics and bibliometrics structurally and contextually is unknown. Therefore, we perform document co-citation network analysis for structural comparison and topic modeling for contextual comparison. We also suggest Spearman’s correlation for statistical comparison. A case study is done for the publications from Journal of the Association for Information Science and Technology and the tweets mentioning the publications. We identified a weak correlation between scholarly impact and social use of these publications. We also found the structures of the traditional citations and Twitter citations share common but high interest in information retrieval system and impact analysis, while Twitter citations have diverse interest in data mining, network analysis, and information behavior as well. In addition, from content analysis, we found the two citation patterns to have both common and distinct characteristics. Specifically, the topics covered by both citation patterns show intersections and exclusive contexts. In conclusion, the traditional citation patterns and the Twitter citation patterns in Information Science are different statistically, structurally, and contextually. We suspect that intentional and unintentional citing behaviors are the main factor for the thematic difference and will be examined on the future.
机译:社交媒体作为一种新兴的交流渠道已经引起了学术界的关注。该渠道为衡量社交媒体中学术出版物对社会使用的影响提供了新的机会(高度度量),这补充了我们对出版物学术影响(文献计量学)的理解。已知两种不同的渠道,即社交媒体和期刊,可以统计地建立各种引用模式。但是,高度计量学和文献计量学在结构上和上下文上的主题差异是未知的。因此,我们进行文档共引网络分析以进行结构比较,并进行主题建模以进行上下文比较。我们还建议使用Spearman的相关性进行统计比较。对《信息科学与技术协会杂志》的出版物以及提及这些出版物的推文进行了案例研究。我们确定了这些出版物的学术影响力和社会用途之间的弱关联。我们还发现,传统引文和Twitter引文的结构在信息检索系统和影响分析方面具有共同但高度的兴趣,而Twitter引文在数据挖掘,网络分析和信息行为方面也具有多种兴趣。另外,通过内容分析,我们发现两种引文模式具有共同特征和独特特征。具体来说,这两种引用模式所涵盖的主题均显示出交叉点和互斥上下文。总之,信息科学中的传统引文模式和Twitter引文模式在统计,结构和上下文方面都不同。我们怀疑有意和无意的引用行为是造成主题差异的主要因素,并将在以后进行研究。

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