Abstract
This study aims to examine how the follower–followee ratio determines user characteristics on the academic social networking site ResearchGate (RG) and to examine institutional participation differences among research universities. It uses the follower–followee ratio as the categorization measure for grouping 87,083 RG users from 61 U.S. universities, in three research activity levels as determined by The Carnegie Classification of Institutions of Higher Education (2016). As a result of analysis, individuals in the sample were further differentiated into three categories or user groups based on the follower–followee ratio: Information Source users (37.98%), Friend users (54.21%), and Information Seeker users (7.81%). These three user categories differ in overall scholarly reputation, popularity, and academic influence with a decrease from Information Source users to Information Seeker users. This study also reveals the current status of institutional participation in terms of activity level, and differences in user composition at three research activity levels. While the proportion of the Information Seeker users remains roughly the same across research activity levels, as the scholarly reputation of a university increases, there is an increase in the proportion of Friend users. The results help promote a deeper understanding of the follower–followee relationship among users on an academic social networking site, as well as the institutional user participation status. Future research should consider an international comparison between nations and disciplines. Application of this approach to other academic social networking sites would enhance general understanding of academic social networking sites and their users.
Similar content being viewed by others
References
Abdulhayoglu, M. A., & Thijs, B. (2017). Use of ResearchGate and Google CSE for author name disambiguation. Scientometrics, 111(3), 1965–1985.
Adali, S., & Golbeck, J. (2014). Predicting personality with social behavior: A comparative study. Social Network Analysis and Mining, 4(1), 1–20.
Al-Daihani, S. M., & AlAwadhi, S. A. (2015). Exploring academic libraries’ use of Twitter: A content analysis. The Electronic Library, 33(6), 1002–1015.
Armentano, M. G., Godoy, D., & Amandi, A. A. (2013). Followee recommendation based on text analysis of micro-blogging activity. Information Systems, 38(8), 1116–1127.
Arshad, A., & Ameen, K. (2017). Scholarly communication in the age of Google: Exploring academics’ use patterns of e-journals at the University of the Punjab. The Electronic Library, 35(1), 167–184.
Bai, S., Zhu, T., & Cheng, L. (2012). Big-five personality prediction based on user behaviors at social network sites. arXiv preprint arXiv:1204.4809.
Benevenuto, F., Magno, G., Rodrigues, T., & Almeida, V. (2010). Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS) (Vol. 6, p. 12).
Bhardwaj, R. K. (2017). Academic social networking sites: Comparative analysis of ResearchGate, Academia.edu, Mendeley and Zotero. Information and Learning Science, 118(5/6), 298–316.
Bianchini, L. (2012). Social networks for scientists: What social media to use for your research activity. Retrieved from https://www.mysciencework.com/omniscience/social-networks-for-scientists.
Borrego, Á. (2017). Institutional repositories versus ResearchGate: The depositing habits of Spanish researchers. Learned Publishing, 30, 185–192.
Campos-Freire, F., & Rúas-Araújo, J. (2016). The use of professional and scientific social networks: The case of three Galician universities. El Profesional de la Informacion, 25(3), 431–440.
Chakraborty, N. (2012). Activities and reasons for using social networking sites by research scholars in NEHU: A study on Facebook and ResearchGate. In Planner-2012 (pp. 19–27).
Citrome, L. (2015). My two favourite professional social networking sites: LinkedIn and ResearchGate—How they can help you, or hurt you. International Journal of Clinical Practice, 69(6), 623–624.
Corvello, V., Genovese, A., & Verteramo, S. (2014). Knowledge sharing among users of scientific social networking platforms. In G. Phillips-Wren, S. Carlsson, A. Respício, & P. Brézillon (Eds.), DSS 2.0—Supporting decision making with new technologies (pp. 369–380). Retrieved from http://ebooks.iospress.nl/volumearticle/36223.
Ebrahim, N. A. (2017). ResearchGate and Academia: Networks for researchers to improve research impact. Retrieved from https://works.bepress.com/aleebrahim/185/download/.
Elsayed, A. M. (2016). The use of academic social networks among Arab researchers: A survey. Social Science Computer Review, 34(3), 378–391.
Gayo-Avello, D., & Brenes, D. J. (2010). Overcoming spammers in Twitter—A tale of five algorithms. In Actas del I Congreso Español de Recuperación de Información (CERI 2010) (pp. 41–52). Madrid, España, 15 y 16 de junio de 2010.
Gruzd, A., & Goertzen, M. (2013). Wired academia: Why social science scholars are using social media. In 2013 46th Hawaii international conference on system sciences (HICSS) (pp. 3332–3341). IEEE.
Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340–2350.
Henning, V., & Reichelt, J. (2008). Mendeley—A Last. fm for research? In IEEE fourth international conference on eScience, 2008. eScience’08 (pp. 327–328). IEEE.
Hoffmann, C. P., Lutz, C., & Meckel, M. (2016). A relational altmetric? Network centrality on ResearchGate as an indicator of scientific impact. Journal of the Association for Information Science and Technology, 67(4), 765–775.
Hu, C. P., Yan, W. W., & Hu, Y. (2015). User satisfaction evaluation of microblogging services in China: Using the tetra-class model. Behaviour and Information Technology, 34(1), 17–32.
Huberman, B., Romero, D. M., & Wu, F. (2009). Social networks that matter: Twitter under the microscope. First Monday. https://doi.org/10.5210/fm.v14i1.2317.
Hull, D., Pettifer, S. R., & Kell, D. B. (2008). Defrosting the digital library: Bibliographic tools for the next generation web. PLoS Computational Biology, 4(10), e1000204.
Jamali, H. R. (2017). Copyright compliance and infringement in ResearchGate full-text journal articles. Scientometrics, 112, 241–254.
Java, A., Song, X., Finin, T., & Tseng, B. (2009). Why we twitter: An analysis of a microblogging community. Advances in web mining and web usage analysis (pp. 118–138). Berlin: Springer.
Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27(1), 365–372.
Kraker, P., & Lex, E. (2015). A critical look at the ResearchGate score as a measure of scientific reputation. In Proceedings of the quantifying and analysing scholarly communication on the web workshop (ASCW’15), Web Science conference.
Kramer, B., & Bosman, J. (2016). Innovations in scholarly communication-global survey on research tool usage. F1000Research, 5, 692.
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008). A few chirps about twitter. In Proceedings of the first workshop on online social networks (pp. 19–24). ACM.
Laakso, M., Lindman, J., Shen, C., Nyman, L., & Björk, B. C. (2017). Research output availability on academic social networks: implications for stakeholders in academic publishing. Electronic Markets, 27(2), 125–133.
Leavitt, A., Burchard, E., Fisher, D., & Gilbert, S. (2009). The influentials: New approaches for analyzing influence on twitter. Web Ecology Project, 4(2), 1–18.
Liu, Z. (2003). Trends in transforming scholarly communication and their implications. Information Processing and Management, 39(6), 889–898.
Lovett, J., Rathemacher, A., Boukari, D., & Lang, C. (2017). Institutional repositories and academic social networks: Competition or complement? A study of open access policy compliance vs. ResearchGate participation. Journal of Librarianship and Scholarly Communication, 5, eP2183.
Martín-Martín, A., Orduna-Malea, E., Harzing, A. W., & López-Cózar, E. D. (2017). Can we use Google Scholar to identify highly-cited documents? Journal of Informetrics, 11(1), 152–163.
Mas-Bleda, A., Thelwall, M., Kousha, K., & Aguillo, I. (2013). European highly cited scientists’ presence in the social web. In 14th International society of scientometrics and informetrics conference (ISSI 2013) (pp. 98–109).
Mas-Bleda, A., Thelwall, M., Kousha, K., & Aguillo, I. F. (2014). Do highly cited researchers successfully use the social web? Scientometrics, 101(1), 337–356.
Morrison, A. (2010). The social parameters of scholarship. ESC: English Studies in Canada, 36(4), 18–21.
Muscanell, N., & Utz, S. (2017). Social networking for scientists: An analysis on how and why academics use ResearchGate. Online Information Review, 41(5), 744–759.
Nicholas, D., Clark, D., & Herman, E. (2016). ResearchGate: Reputation uncovered. Learned Publishing, 29(3), 173–182.
Nicholas, D., Herman, E., Jamali, H. R., Bravo, B. R., Boukacem-Zeghmouri, C., Dobrowolski, T., et al. (2015). New ways of building, showcasing, and measuring scholarly reputation. Learned Publishing, 28(3), 169–183.
Onyancha, O. B. (2015). Social media and research: An assessment of the coverage of South African universities in ResearchGate, Web of Science and the Webometrics Ranking of World Universities. South African Journal of Libraries and Information Science, 81(1), 8–20.
Orduna-Malea, E., Martín-Martín, A., Thelwall, M., & Delgado López-Cózar, E. (2017). Do ResearchGate Scores create ghost academic reputations? Scientometrics, 112(1), 443–460.
Pallant, J. (2005). SPSS survival manual: A step by step guide to using SPSS for windows (version 12). Crows Nest, NSW: Allen and Unwin.
Pirshahid, S. E., Naghshineh, N., & Fahimnia, F. (2016). Knowledge and use of Web 2.0 by librarians in university libraries of East Azerbaijan, Iran. The Electronic Library, 34(6), 1013–1030.
Priem, J., Taraborelli, D., Groth, P., & Neylon, C. (2010). Altmetrics: A manifesto. Retrieved from http://altmetrics.org/manifesto/.
ResearchGate. (2017). About ResearchGate. Retrieved from https://www.researchgate.net/about.
Shrivastava, R., & Mahajan, P. (2017). An altmetric analysis of ResearchGate profiles of physics researchers: A study of University of Delhi (India). Performance Measurement and Metrics, 18(1), 52–66.
StatisticsSolutions. (2017). Kruskal–Wallis test. Retrieved from http://www.statisticssolutions.com/kruskal-wallis-test/.
The Carnegie Classification of Institutions of Higher Education. (2016). About Carnegie classification. Retrieved (September 22, 2016) from http://carnegieclassifications.iu.edu/.
Thelwall, M., & Kousha, K. (2014). Academia.edu: Social network or academic network? Journal of the Association for Information Science and Technology, 65(4), 721–731.
Thelwall, M., & Kousha, K. (2015a). Web indicators for research evaluation. Part 2: Social media metrics. El Profesional de la Información, 24(5), 607–620.
Thelwall, M., & Kousha, K. (2015b). ResearchGate: Disseminating, communicating, and measuring Scholarship? Journal of the Association for Information Science and Technology, 66(5), 876–889.
Thelwall, M., & Kousha, K. (2017). ResearchGate articles: Age, discipline, audience size, and impact. Journal of the Association for Information Science and Technology, 68(2), 468–479.
Tommasel, A., Corbellini, A., Godoy, D., & Schiaffino, S. (2015). Exploring the role of personality traits in followee recommendation. Online Information Review, 39(6), 812–830.
U.S. News. (2016). National Universities Rankings. Retrieved from http://colleges.usnews.rankingsandreviews.com/best-colleges/rankings/national-universities.
Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129.
Veletsianos, G. (2013). Open practices and identity: Evidence from researchers and educators’ social media participation. British Journal of Educational Technology, 44(4), 639–651.
Westerman, D., Spence, P. R., & Van Der Heide, B. (2012). A social network as information: The effect of system generated reports of connectedness on credibility on Twitter. Computers in Human Behavior, 28(1), 199–206.
Williams, A. E., & Woodacre, M. A. (2016). The possibilities and perils of academic social networking sites. Online Information Review, 40(2), 282–294.
Wu, M. X. (2010). Twitter under microscope. Retrieved from http://socialbeta.com/t/twitter-under-microscope.html.
Xia, F., Su, X., Wang, W., Zhang, C., Ning, Z., & Lee, I. (2016). Bibliographic analysis of nature based on Twitter and Facebook altmetrics data. PLoS ONE, 11(12), e0165997.
Yardi, S., Romero, D., & Schoenebeck, G. (2010). Detecting spam in a twitter network. First Monday. https://doi.org/10.5210/fm.v15i1.2793.
Yu, M. C., Wu, Y. C. J., Alhalabi, W., Kao, H. Y., & Wu, W. H. (2016). ResearchGate: An effective altmetric indicator for active researchers? Computers in Human Behavior, 55, 1001–1006.
Funding
This work was supported by the Chinese National Funds of Social Science (No. 15CTQ025) and the School of Information Management, Wuhan University through the funding “World-Class Discipline of the Chinese Ministry of Education – Library and Information Science, and Data Science.”
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Category | ID | Universities | Rank | Total no. | Information source no. | Friend no. | Information seeker no. |
---|---|---|---|---|---|---|---|
R1 | R101 | Princeton University | 1 | 1135 | 487 | 578 | 70 |
R102 | Harvard University | 2 | 3254 | 1344 | 1705 | 205 | |
R103 | University of Chicago | 3 | 1917 | 670 | 1102 | 145 | |
R104 | Yale University | 3 | 3222 | 1079 | 1888 | 255 | |
R105 | Columbia University | 5 | 3495 | 1355 | 1807 | 333 | |
R106 | Stanford University | 5 | 4891 | 1877 | 2688 | 326 | |
R107 | Massachusetts Institute of Technology | 7 | 3433 | 1451 | 1736 | 246 | |
R108 | Duke University | 8 | 2375 | 817 | 1356 | 202 | |
R109 | University of Pennsylvania | 8 | 3835 | 1276 | 2236 | 323 | |
R110 | Johns Hopkins University | 10 | 2362 | 875 | 1265 | 222 | |
R111 | California Institute of Technology | 12 | 1311 | 485 | 724 | 102 | |
R112 | Northwestern University | 12 | 3023 | 1005 | 1777 | 241 | |
R113 | Brown University | 14 | 1228 | 477 | 643 | 108 | |
R114 | Cornell University | 15 | 2276 | 994 | 1103 | 179 | |
R115 | Rice University | 15 | 854 | 336 | 458 | 60 | |
R116 | University of Notre Dame | 15 | 825 | 326 | 450 | 49 | |
R117 | Vanderbilt University | 15 | 2869 | 898 | 1763 | 208 | |
R118 | Washington University in St. Louis | 19 | 2890 | 907 | 1722 | 261 | |
R119 | Emory University | 20 | 2432 | 751 | 1467 | 214 | |
R120 | Georgetown University | 20 | 876 | 359 | 442 | 75 | |
R121 | University of California, Berkeley | 20 | 3428 | 1470 | 1711 | 247 | |
R2 | R201 | Dartmouth College | 11 | 763 | 296 | 416 | 51 |
R202 | Wake Forest University | 27 | 291 | 124 | 145 | 22 | |
R203 | College of William and Mary | 32 | 255 | 118 | 120 | 17 | |
R204 | Rensselaer Polytechnic Institute | 39 | 593 | 268 | 291 | 34 | |
R205 | Lehigh University | 44 | 419 | 173 | 217 | 29 | |
R206 | Southern Methodist University | 56 | 329 | 148 | 155 | 26 | |
R207 | Worcester Polytechnic Institute | 60 | 315 | 142 | 143 | 30 | |
R208 | Yeshiva University | 66 | 160 | 65 | 80 | 15 | |
R209 | Brigham Young University-Provo | 68 | 923 | 402 | 448 | 73 | |
R210 | Baylor University | 71 | 377 | 156 | 199 | 22 | |
R211 | Stevens Institute of Technology | 71 | 309 | 128 | 150 | 31 | |
R212 | American University Washington DC | 74 | 295 | 129 | 149 | 17 | |
R213 | Miami University | 79 | 523 | 205 | 274 | 44 | |
R214 | Colorado School of Mines | 82 | 398 | 170 | 189 | 39 | |
R215 | Texas Christian University | 82 | 240 | 102 | 118 | 20 | |
R216 | Binghamton University-SUNY | 86 | 454 | 178 | 250 | 26 | |
R217 | Marquette University | 86 | 386 | 166 | 195 | 25 | |
R218 | University of Denver | 86 | 367 | 161 | 179 | 27 | |
R219 | University of Tulsa | 86 | 184 | 81 | 92 | 11 | |
R220 | University of Vermont | 92 | 881 | 318 | 469 | 94 | |
R3 | R301 | Pepperdine University | 50 | 92 | 49 | 31 | 12 |
R302 | Texas Wesleyan University | 50 | 11 | 8 | 3 | 0 | |
R303 | Fairleigh Dickinson University | 67 | 94 | 33 | 56 | 5 | |
R304 | Clark University | 74 | 140 | 63 | 62 | 15 | |
R305 | University of San Diego | 86 | 161 | 77 | 69 | 15 | |
R306 | SUNY College of Environmental Science and Forestry | 99 | 132 | 58 | 61 | 13 | |
R307 | Rochester Institute of Technology | 107 | 437 | 202 | 205 | 30 | |
R308 | University of San Francisco | 107 | 194 | 90 | 95 | 9 | |
R309 | Drew University | 108 | 44 | 24 | 19 | 1 | |
R310 | University of the Pacific (California, USA) | 111 | 158 | 67 | 81 | 10 | |
R311 | Seton Hall University | 118 | 184 | 85 | 90 | 9 | |
R312 | University of St. Thomas | 118 | 138 | 53 | 69 | 16 | |
R313 | DePaul University | 124 | 310 | 131 | 149 | 30 | |
R314 | Clarkson University | 129 | 229 | 85 | 130 | 14 | |
R315 | Hofstra University | 133 | 176 | 80 | 88 | 8 | |
R316 | Mercer University | 135 | 156 | 67 | 82 | 7 | |
R317 | Adelphi University | 146 | 147 | 56 | 76 | 15 | |
R318 | St. John Fisher College | 146 | 56 | 23 | 28 | 5 | |
R319 | Immaculata University | 152 | 10 | 3 | 4 | 3 | |
R320 | University of La Verne | 152 | 42 | 19 | 20 | 3 |
Rights and permissions
About this article
Cite this article
Yan, W., Zhang, Y. & Bromfield, W. Analyzing the follower–followee ratio to determine user characteristics and institutional participation differences among research universities on ResearchGate. Scientometrics 115, 299–316 (2018). https://doi.org/10.1007/s11192-018-2637-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-018-2637-6