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How do academic topics shift across altmetric sources? A case study of the research area of Big Data

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Abstract

Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easy-to-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.

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Notes

  1. “Rio + 20” refers to the United Nations Conference on Sustainable Development, which was held in Rio de Janeiro, Brazil on 20–22 June 2012. https://www.environment.gov.au/about-us/international/rio-20

  2. In our view, Policy document mentions may not be seen as strictly social media events (see also Wouters et al. 2018); however we decided still to include them in this study as a relevant source by itself in capturing forms of policy-related impact (Bornmann et al. 2016).

  3. https://www.altmetric.com. The data from Altmetric.com used in this study is updated up to October 2017.

  4. Considering the short titles of Wikipedia articles, we choose to use the first sentence in the summary which is a condensed explanation of an event, and is equivalent to the titles of blogs, news and policy documents in part.

  5. This decision is also backed up by the results observed by Robinson-Garcia et al (2017) in which they found relatively low levels of engagement of tweeters with publication, therefore limiting the value of a semantic study based only on tweets’ full text.

  6. We can argue that these topics are added by the online users, thus “expanding” or “amplifying” the initial topics put forward by the authors through the author keywords. It could also be argued, that these topics added by the online users are also a sort of “reinterpretation” of the academic topics of the papers.

  7. VOSviewer is used for clustering author keywords: a resolution of 0.5 is employed in the clustering algorithm, with minimal cluster size of 1 item, and the option “merge small clusters” is enabled. The “association strength” is applied for normalization. Default values are used for layout.

  8. Map-reduce and Hadoop are the two leading tools related with machine learning and cloud computing (Zhang et al. 2019).

  9. The online platform WordItOut (https://worditout.com/word-cloud/create) is used for showing the word cloud layouts in our study.

References

  • Alampi, A. (2012). Social media is more than simply a marketing tool for academic research. The Guardian. Retrieved from https://www.theguardian.com/higher-education-network/blog/2012/jul/24/social-media-academic-research-tool. Accessed 23 Apr 2019.

  • Allen, H., Stanton, T., Pietro, F., & Moseley, G. (2013). Social media release increases dissemination of original articles in the clinical pain sciences. PLoS ONE,8(7), e68914.

    Article  Google Scholar 

  • Alperin, J. P. (2015). Geographic variation in social media metrics: An analysis of Latin American journal articles. Aslib Journal of Information Management,67(3), 289–304.

    Article  Google Scholar 

  • Altmetric. (2016). How is the altmetric attention score calculated? Retrieved from https://help.altmetric.com/support/solutions/articles/6000060969-how-is-the-altmetric-score-calculated. Accessed 21 Apr 2019.

  • An, X., Ganguly, A., Fang, Y., Scyphers, S., Hunter, A., & Dy, J. (2014). Tracking climate change opinions from Twitter data. In Workshop on data science for social good (pp. 1–6). Retrieved from https://dssg.uchicago.edu/kddworkshop/papers/dy.pdf. Accessed 23 Apr 2019.

  • Bartling, S., & Friesike, S. (2014). Opening science: The evolving guide on how the internet is changing research, collaboration and scholarly publishing. Heidelberg: Springer.

    Book  Google Scholar 

  • Bornmann, L. (2014). Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics. Journal of Informetrics,8(4), 895–903.

    Article  Google Scholar 

  • Bornmann, L., & Haunschild, R. (2017). Does evaluative scientometrics lose its main focus on scientific quality by the new orientation towards societal impact? Scientometrics,110(2), 937–943.

    Article  Google Scholar 

  • Bornmann, L., Haunschild, R., & Marx, W. (2016). Policy documents as sources for measuring societal impact: How often is climate change research. Scientometrics,109(3), 1477–1495. https://doi.org/10.1007/s11192-016-2115-y.

    Article  Google Scholar 

  • Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of 43rd Hawaii international conference on system sciences (pp. 1–10). IEEE.

  • Costas, R. (2018). Towards the social media studies of science: Social media metrics, present and future. arXiv:1801.04437.

  • Costas, R., van Honk, J., & Franssen, T. (2017). Scholars on Twitter: Who and how many are they?. arXiv:1712.05667.

  • Costas, R., Zahedi, Z., & Wouters, P. (2015). The thematic orientation of publications mentioned on social media: Large-scale disciplinary comparison of social media metrics with citations. Aslib Journal of Information Management,67, 260–288.

    Article  Google Scholar 

  • Cronin, B., & Sugimoto, C. R. (Eds.). (2015). Scholarly metrics under the microscope: From citation analysis to academic auditing (pp. 933–940). Association for Information Science and Technology by Information Today, Incorporated.

  • Fraumann, G., Zahedi, Z., & Costas, C. R. (2015). What do we know about Altmetric.com sources? A study of the top 200 blogs and news sites mentioning scholarly output. In Proceedings of theAltmetrics workshop. Amsterdam Science Park, Amsterdam.

  • Gupta, D., & Rani, R. (2019). A study of big data evolution and research challenges. Journal of Information Science,45(3), 322–340.

    Article  Google Scholar 

  • Haunschild, R., & Bornmann, L. (2015). F1000Prime: An analysis of discipline-specific reader data from Mendeley. F1000Research,4, 41.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Haustein, S. (2016). Grand challenges in altmetrics: Heterogeneity, data quality and dependencies. Scientometrics,108(1), 413–423.

    Article  Google Scholar 

  • Haustein, S., Bowman, T. D., & Costas, R. (2016). Interpreting “altmetrics”: Viewing acts on social media through the lens of citation and social theories. In C. R. Sugimoto (Ed.), Theories of informetrics and scholarly communication: A Festschrift in Honor of Blaise Cronin (pp. 372–405). Berlin: De Gruyter Mouton.

    Chapter  Google Scholar 

  • Haustein, S., Bowman, T. D., Holmberg, K., Peters, I., & Larivière, V. (2014). Astrophysicists on Twitter. Aslib Journal of Information Management,66(3), 279–296.

    Article  Google Scholar 

  • Haustein, S., Costas, R., & Larivière, V. (2015). Characterizing social media metrics of scholarly papers: The effect of document properties and collaboration patterns. PLoS ONE,10(3), 2–3.

    Article  Google Scholar 

  • Hellsten, I., & Leydesdorff, L. (2017). Automated analysis of topic-actor networks on twitter: New approach to the analysis of socio-semantic networks. arXiv:1711.08387.

  • Huang, A. (2008). Similarity measures for text document clustering. In Proceedings of the sixth New Zealand computer science research student conference (NZCSRSC2008). (Vol. 4, pp. 9–56). Christchurch, New Zealand .

  • Kacfah Emani, C., Cullot, N., & Nicolle, C. (2015). Understandable big data: A survey. Computer Science Review,17, 70–81.

    Article  MathSciNet  Google Scholar 

  • Kouper, I. (2010). Science blogs and public engagement with science: Practices, challenges, and opportunities. Journal of Science Communication,9(1), 1–10.

    Article  Google Scholar 

  • Larivière, V., Ni, C. C., Gingras, Y., Cronin, B., & Sugimoto, C. R. (2013). Bibliometrics: Global gender disparities in science. Nature News,504(7479), 211.

    Article  Google Scholar 

  • Leek, J. (2013). Six types of analyses every data science should know. Data scientist Insights blog. Retrieved from https://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientistshould-know/. Accessed 25 May 2019.

  • Maflahi, N., & Thelwall, M. (2018). How quickly do publications get read? The evolution of mendeley reader counts for new articles. Journal of the Association for Information Science and Technology,69(1), 158–167.

    Article  Google Scholar 

  • Mansour, E. A. (2015). The use of social networking sites (SNSs) by the faculty members of the school of library and information science, PAAET, Kuwait. The Electronic Library,33(3), 524–546.

    Article  Google Scholar 

  • McCaughey, D., Baumgardner, C., Gaudes, A., LaRochelle, D., Wu, K. J., & Raichura, T. (2014). Best practices in social media: Utilizing a value matrix to assess social media’s impact on health care. Social science computer review,32(5), 575–589.

    Article  Google Scholar 

  • Nerghes, A., & Lee, J. S. (2018). The refugee/migrant crisis dichotomy on twitter: A network and sentiment perspective. In Proceedings of the 10th ACM conference on web science (pp. 271–280). ACM.

  • Nicholas, D., Watkinson, A., Volentine, R., Allard, S., Levine, K., Tenopir, C., et al. (2014). Trust and authority in scholarly communications in the light of the digital transition: Setting the scene for a major study. Learned Publishing,27(2), 121–134.

    Article  Google Scholar 

  • Park, H. W., & Leydesdorff, L. (2013). Decomposing social and semantic networks in emerging “big data” research. Journal of Informetrics,7(3), 756–765.

    Article  Google Scholar 

  • Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2014). Climate change on twitter: Topics, communities and conversations about the 2013 IPCC Working Group 1 report. PLoS ONE,9(4), 1–11.

    Article  Google Scholar 

  • Phillips, F. (2017). A perspective on ‘Big Data’. Science and Public Policy,44(5), 730–737.

    Article  Google Scholar 

  • Priem, J., Costello, K., & Dzuba, T. (2012). Prevalence and use of Twitter among scholars. In Presented at the metrics 2011 symposium on informetric and scientometric research. Retrieved from https://doi.org/10.6084/m9.figshare.104629. Accessed 10 Aug 2019.

  • Robin-Songarcia, N., Arroyo-Machado, W., & Torres-Salinas, D. (2019). Mapping social media attention in microbiology: Identifying main topics and actors. FEMS Microbiology letters,366(7), fnz075.

    Article  Google Scholar 

  • Robinson-Garcia, N., Costas, R., Isett, K., Melkers, J., & Hicks, D. (2017). The unbearable emptiness of tweeting-about journal articles. PLoS ONE,12(8), 1–19.

    Article  Google Scholar 

  • Rowlands, I., Nicholas, D., Russell, B., Canty, N., & Watkinson, A. (2011). Social media use in the research workflow. Learned Publishing,24(3), 183–195.

    Article  Google Scholar 

  • Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Information Science and Technology,68(9), 2037–2062.

    Article  Google Scholar 

  • Thelwall, M. (2017). Are Mendeley reader counts useful impact indicators in all fields? Scientometrics,113(3), 1721–1731.

    Article  Google Scholar 

  • Thelwall, M., Haustein, S., Larivière, V., & Sugimoto, C. R. (2013). Do altmetrics work? Twitter and ten other social web services. PLoS ONE,8(5), e64841.

    Article  Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics,84(2), 523–538.

    Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer (Digital Libraries). ISSI Newsletter (Vol. 7).https://doi.org/10.1371/journal.pone.0054847

  • Van Eck, N. J., Waltman, L., Dekker, R., & Van den Berg, J. (2010a). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology,61(12), 2405–2416.

    Article  Google Scholar 

  • Van Eck, N. J., Waltman, L., Noyons, E. C. M., & Buter, R. K. (2010b). Automatic term identification for bibliometric mapping. Scientometrics,82(3), 581–596.

    Article  Google Scholar 

  • Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature news,512(7513), 126.

    Article  Google Scholar 

  • Walker, J. (2006). Blogging from inside the ivory tower. In A. Bruns & J. Jacobs (Eds.), Uses of blogs (pp. 127–138). New York, NY: Peter Lang. Retrieved from https://bora.uib.no/handle/1956/1846. Accessed 21 Apr 2019.

  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics,34(2), 77–84.

    Article  Google Scholar 

  • Waltman, L., Van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics,4(4), 629–635.

    Article  Google Scholar 

  • Wouters, P., & Costas, R. (2012). Users, narcissism and control: Tracking the impact of scholarly publications in the 21st century (pp. 847–857). Utrecht: SURF foundation.

    Google Scholar 

  • Wouters, P., Zahedi, Z., & Costas, R. (2018). Social media metrics for new research evaluation. arXiv:1806.10541.

  • Yu, H., Xiao, T., Xu, S., & Wang, Y. (2019). Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters. Journal of Informetrics,13(3), 841–855.

    Article  Google Scholar 

  • Zahedi, Z., Costas, R., & Wouters, P. (2014). Assessing the impact of publications saved by Mendeley users: Is there any different pattern among users? In Proceedings of theIATUL Conference, Espoo, Finland, June 2–5, 2014. Retrieved from https://docs.lib.purdue.edu/iatul/2014/altmetrics/4.. Accessed 21 Apr 2019.

  • Zhang, Y., Huang, Y., Porter, A. L., Zhang, G., & Lu, J. (2019). Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study. Technological Forecasting and Social Change,146, 795–807.

    Article  Google Scholar 

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Acknowledgements

Xiaozan Lyu was supported by China Scholarship Council (CSC Student ID 201806320214), and the National Science Foundation of China (NSFC, Grant No. 71843012). Rodrigo Costas was partially supported by funding from the DST-NRF Centre of Excellence in Scientometrics and Science, Technology and Innovation Policy (SciSTIP) (South Africa). The authors thank an anonymous reviewer for the insightful comments of an early version of the paper. The authors also acknowledge Altmetric.com for providing the altmetric data of scientific publications for this study.

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Authors and Affiliations

  1. Department of Information Resources Management, School of Public Affairs, Zhejiang University, Hangzhou, 310058, China

    Xiaozan Lyu

  2. Center for Science and Technology Studies (CWTS), Leiden University, Kolffpad 1, P.O. Box 905, 2300 AX, Leiden, The Netherlands

    Xiaozan Lyu & Rodrigo Costas

  3. Centre for Research on Evaluation, Science and Technology (CREST), Stellenbosch University, RW Wilcocks Building, Stellenbosch, 7600, South Africa

    Rodrigo Costas

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Correspondence to Xiaozan Lyu.

Appendix

Appendix

See Figs. 13 and 14 and Tables 8 and 9.

Fig. 13
figure 13

Term maps of blog, news, policy and wikipedia. The minimum number of occurrence for being plotted is 3 for terms from blogs and news, and 2 for terms from policy documents and wikipedia articles

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Fig. 14
figure 14

Venn diagram of topic sets in six groups

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Table 8 235 topics appeared in at least one group and their occurrence in each group
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Table 9 Cosine similarity of topic sets in six groups
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Lyu, X., Costas, R. How do academic topics shift across altmetric sources? A case study of the research area of Big Data. Scientometrics 123, 909–943 (2020). https://doi.org/10.1007/s11192-020-03415-7

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