Library and Information Science Papers Discussed on Twitter: A new Network-based Approach for Measuring Public Attention

doi:10.2478/jdis-2020-0017

Abstract

Abstract:

Purpose: In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society (beyond science). We address the following question: can Twitter be meaningfully used for the mapping of public and scientific discourses?

Design/methodology/approach: Recently, Haunschild et al. (2019) introduced a new network-oriented approach for using Twitter data in research evaluation. Such a procedure can be used to measure the public discussion around a specific field or topic. In this study, we used all papers published in the Web of Science (WoS, Clarivate Analytics) subject category Information Science & Library Science to explore the publicly discussed topics from the area of library and information science (LIS) in comparison to the topics used by scholars in their publications in this area.

Findings: The results show that LIS papers are represented rather well on Twitter. Similar topics appear in the networks of author keywords of all LIS papers, not tweeted LIS papers, and tweeted LIS papers. The networks of the author keywords of all LIS papers and not tweeted LIS papers are most similar to each other.

Research limitations: Only papers published since 2011 with DOI were analyzed.

Practical implications: Although Twitter data do not seem to be useful for quantitative research evaluation, it seems that Twitter data can be used in a more qualitative way for mapping of public and scientific discourses.

Originality/value: This study explores a rather new methodology for comparing public and scientific discourses.

Key words: Altmetrics, Twitter, News, Hashtags, Author

Haunschild, Robin, Loet Leydesdorff, and Lutz Bornmann. "Library and Information Science Papers Discussed on Twitter: A new Network-based Approach for Measuring Public Attention." Journal of Data and Information Science, vol.5, no.3, 2020, pp.5-17. DOI: 10.2478/jdis-2020-0017

Figures/Tables 6

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Table 1

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References 12

1	Bornmann L . ( 2015). Alternative metrics in scientometrics: A meta-analysis of research into three altmetrics. Scientometrics, 103(3), 1123-1144. doi: 10.1007/s11192-015-1565-y. doi: 10.1007/s11192-015-1565-y
2	Bornmann L., & Haunschild R . ( 2016). How to normalize Twitter counts? A first attempt based on journals in the Twitter Index. Scientometrics, 107(3), 1405-1422. doi: 10.1007/s11192-016-1893-6. doi: 10.1007/s11192-016-1893-6
3	Bornmann L., Haunschild R., & Adams J . ( 2019). Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF). Journal of Informetrics, 13(1), 325-340. doi: 10.1016/j.joi.2019.01.008. doi: 10.1016/j.joi.2019.01.008
4	Bornmann L., Haunschild R., & Marx W . ( 2016). Policy documents as sources for measuring societal impact: How often is climate change research mentioned in policy-related documents? Scientometrics, 109(3), 1477-1495. doi: 10.1007/s11192-016-2115-y. doi: 10.1007/s11192-016-2115-y pmid: 27942080
5	Haunschild R., Leydesdorff L., & Bornmann L . ( 2019). Library and Information Science papers as topics on Twitter: A network approach to measuring public attention. Paper presented at the ISSI 2019—17th International Conference of the International Society for Scientometrics and Informetrics, Rome, Italy.
6	Haunschild R., Leydesdorff L., Bornmann L., Hellsten I., & Marx W . ( 2019). Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags. Journal of Informetrics, 13(2), 695-707. doi: 10.1016/j.joi.2019.03.008. doi: 10.1016/j.joi.2019.03.008
7	Haunschild R., Leydesdorff L., Bornmann L., Hellsten I., & Marx W . (2020). Corrigendum to “Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags” [J. Informetrics 13 (2019) 695-707]. Journal of Informetrics, 14(1), February 2020, 101020. doi: 10.1016/j.joi.2020.101020
8	Hellsten I., & Leydesdorff L . ( 2020). Automated analysis of actor-topic networks on twitter: New approaches to the analysis of socio-semantic networks. JASIST, 71(1), 3-15. doi: 10.1002/asi.24207
9	R Core Team . ( 2019). R: A Language and Environment for Statistical Computing (Version 3. 6.0). Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/
10	Robinson-Garcia N., Costas R., Isett K., Melkers J., & Hicks D . ( 2017). The unbearable emptiness of tweeting—About journal articles. PLOS ONE, 12(8), e0183551. doi: 10.1371/journal.pone.0183551. doi: 10.1371/journal.pone.0183551 pmid: 28837664
11	Thelwall M . ( 2018). Early Mendeley readers correlate with later citation counts. Scientometrics, 115(3), 1231-1240. doi: 10.1007/s11192-018-2715-9. doi: 10.1007/s11192-018-2715-9
12	Wouters. P., Zahedi Z., & Costas R . (2019) Social media metrics for new research evaluation. In: Glänzel W., Moed H.F., Schmoch U., Thelwall M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham, pp 687-713.

	All	Not tweeted	Tweeted	Tweeted and mentioned in the news
All	65	85.9%	74.6%	36.9%
Not tweeted	55	64	58.7%	29.7%
Tweeted	47	37	63	44.4%
Tweeted and mentioned in the news	24	19	28	65

[1]	Lawrence Smolinsky, Aaron J Lercher. Co-author Weighting in Bibliometric Methodology and Subfields of a Scientific Discipline [J]. Journal of Data and Information Science, 2020, 5(3): 84-96.
[2]	Kayvan Kousha, Mike Thelwall. An Automatic Method to Identify Citations to Journals in News Stories: A Case Study of UK Newspapers Citing Web of Science Journals [J]. Journal of Data and Information Science, 2019, 4(3): 73-95.
[3]	Carlos Luis González-Valiente, Magda León Santos, Ricardo Arencibia-Jorge. Evolution of the Socio-cognitive Structure of Knowledge Management (1986-2015): An Author Co-citation Analysis [J]. Journal of Data and Information Science, 2019, 4(2): 36-55.
[4]	Stephen F. Carley, Alan L. Porter, Jan L. Youtie. A Multi-match Approach to the Author Uncertainty Problem [J]. Journal of Data and Information Science, 2019, 4(2): 1-18.
[5]	Fernandez Espinosa Daniela,Xiao Lu. Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us? [J]. Journal of Data and Information Science, 2018, 3(1): 40-53.
[6]	van Raan Anthony F.J.. Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research [J]. Journal of Data and Information Science, 2017, 2(1): 13-50.
[7]	Xiaoqiu Le, Chenyu Mao, Yuanbiao He, Changlei Fu & Liyuan Xu. Dpaper: An Authoring Tool for Extractable Digital Papers [J]. Journal of Data and Information Science, 2016, 1(1): 86-97.
[8]	Wenjing HE, Xiaoyu CHEN, Yichen LI, Yueyi QIU. What leads to readers' satisfaction with the mobile news apps service? An investigation into the roles of content originality and user experience [J]. Journal of Data and Information Science, 2015, 7(3): 76-89.
[9]	Simon S. LI & Fred Y. YE. Evaluating journals' yearly impact with altmetric indicators [J]. Journal of Data and Information Science, 2015, 7(2): 25-38.
[10]	Xiaojuan ZHANG & Jian LI. Exploring features for automatic identification of news queries through query logs [J]. Journal of Data and Information Science, 2014, 7(4): 31-45.
[11]	Chunlei YE. Mapping the evolution of research topics using ATM and SNA [J]. Journal of Data and Information Science, 2014, 7(4): 46-62.
[12]	LI Chunming & SONG Lirong . An introductory discussion about National Library of China’s development of a Chinese Newspaper Digital Repository mechanism [J]. Journal of Data and Information Science, 2008, 1(3/4): 147-159.