|
|
|
| Study of Scientific Tweet Author’s Behavior Pattern and Geographic Distribution |
| Yu Houqiang1, Wang Yuefen1, Wang Feifei2, Chen Bikun1 |
1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094;
2. School of Economics and Management, Beijing University of Technology, Beijing 100124 |
|
|
|
|
Abstract Statistical and visualization analyses were conducted on 2.63 million authors of 20.69 million scientific tweets, to reveal the authors’ behavior patterns including the number of scientific tweets, followed sources, and followed disciplines, as well as their geographic distribution at both a country and a city level. Results will provide reference for further understanding of the meaning of Twitter altmetrics and for future applications. Results show: (1) the distribution of authors’ productivity is highly skewed; 10% of the authors produced 80% of scientific tweets, and 91% of the authors tweeted no more than 10 scientific tweets. This means that most authors only occasionally disseminate and discuss academic products on Twitter. Meanwhile, there is a small percentage of extremely active authors. (2) Core sources that attract most authors, such as Nature, The Conversation and PLoS ONE, take up 6% of all publication sources and account for 77% of scientific tweets. Furthermore, 62% of authors follow only one source. (3) Disciplines that attract most authors are Medicine, General and Social Science, with 71% of authors following only 1 discipline while approximately 8% of authors follow over 3 disciplines. (4) Authors of scientific tweets are distributed all over the world, but are especially dense in USA and Europe. In East Asia, Japan is the most prominent country whereas in South America, Brazil is the most prominent. Authors are concentrated in cities like London and New York. These results show that Twitter altmetrics based on pure number of scientific tweets are not effective enough and future practical indicators need to combine author context as an important factor.
|
|
Received: 09 October 2017
|
|
|
|
[1] Hadgu A T.Mining scholarly communication and interaction on the social Web[C]// Proceedings of the 24th International Conference on World Wide Web. New York: ACM Press, 2015: 499-503.
[2] Priem J, Costello K L.How and why scholars cite on Twitter[J]. Proceedings of the American Society for Information Science and Technology, 2010, 47(1): 1-4.
[3] Priem J, Costello K, Dzuba T. First-year graduate students just wasting time? Prevalence and use of Twitter among scholars[C/OL]// Proceedings of the Conference on Metrics 2011 Symposium on Informetric and Scientometric Research, New Orleans, La, USA. http://jasonpriem.org/self-archived/5uni-poster.png.
[4] Weller K, Dröge E, Puschmann C.Citation analysis in Twitter: Approaches for defining and measuring information flows within tweets during scientific conferences[C]// CEUR Workshop Proceedings on Making Sense of Microposts, Workshop at Extended Semantic Web Conference, Crete, Greece, 2011, 718: 1-12.
[5] Haustein S, Costas R, Larivière V.Characterizing social media metrics of scholarly papers: the effect of document properties and collaboration patterns[J]. PLoS ONE, 2015, 10(3): e0120495.
[6] 舒非, Haustein S, 全薇. 推特(Twitter)对中国论文的国际关注度影响研究[J]. 图书馆论坛, 2017, 37(6): 55-60.
[7] Robinson-García N, Torres-Salinas D, Zahedi Z, et al.New data, new possibilities: exploring the insides of altmetric. com[J]. El Profesional de la Información, 2014, 23(4): 359-366.
[8] Yu H.Context of altmetrics data matters: an investigation of count type and user category[J]. Scientometrics, 2017, 111(1): 267-283.
[9] Yu H, Xu S, Xiao T, et al.Global science discussed in local altmetrics: Weibo and its comparison with Twitter[J]. Journal of Informetrics, 2017, 11(2): 466-482.
[10] Thelwall M, Haustein S, Larivière V, et al.Do altmetrics work? Twitter and ten other social Web services[J]. PLoS ONE, 2013, 8(5): e64841.
[11] Bornmann L, Haunschild R. t factor: A metric for measuring impact on Twitter[J]. Malaysian Journal of Library & Information Science, 2016, 21(2): 13-20.
[12] Thelwall M, Tsou A, Weingart S, et al.Tweeting links to academic articles[J]. Cybermetrics, 2013, 17(1): 1-8.
[13] Haustein S, Bowman T D, Holmberg K, et al.Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter[J]. Journal of the Association for Information Science and Technology, 2015, 67(1): 232-238.
[14] Bornmann L.What do altmetrics counts mean? A plea for content analyses[J]. Journal of the Association for Information Science and Technology, 2016, 67(4): 1016-1017.
[15] Tsou A, Bowman T, Ghazinejad A, et al.Who tweets about science?[C]// Proceedings of the 2015 International Society for Scientometrics and Informetrics, Istanbul, Turkey, 2015: 95-100.
[16] Haustein S, Bowman T, Costas R.Communities of attention around scientific publications: Who is tweeting about scientific papers? [EB/OL]. [2015-7-18].https://www.slideshare.net/StefanieHaustein/ communities-of-attention-around-journal-papers-who-is-tweeting- about-scientific-publications.
[17] Holmberg K, Thelwall M.Disciplinary differences in Twitter scholarly communication[J]. Scientometrics, 2014, 101(2): 1027-1042.
[18] 余厚强, Hemminger B M, 肖婷婷, 等. 新浪微博替代计量指标特征分析[J]. 中国图书馆学报, 2016, 42(4): 20-36.
[19] Haustein S, Costas R.Identifying Twitter audiences: Who is tweeting about scientific papers?[EB/OL]. [2017-4-15].https:// www.asist.org/SIG/SIGMET/wp-content/uploads/2015/10/sigmet2015_paper_11.pdf.
[20] Leetaru K, Wang S W, Padmanabhan A, et al.Mapping the global twitter heartbeat: The geography of Twitter[J]. First Monday, 2013, 18(5): 10-16. |
|
|
|
