Understanding the Usage Characteristics of Twitter in the UK Universities: A Social Network Analysis (SNA) Approach

  • Ufuk Bakan Assistant Professor (PhD), İzmir Kâtip Çelebi University, Izmir
  • Uğur Bakan Assistant Professor(PhD), İzmir Kâtip Çelebi University, Izmir, Turkey
  • Turgay Kan Assoc. Professor(PhD), Ordu University, Ordu, Turkey
Keywords: Social Network Analysis, Twitter, UK, high education


The rate of use of social media platforms such as Facebook, Twitter, and LinkedIn has increased drastically over the last decade. Twitter is the eighth most popular website in the world, with an average of nearly eleven million hits a day. Twitter may be used for synchronous and asynchronous online conversations, asking and answering questions, and sharing opinions, ideas, and resources. Twitter also offers a platform for quick communication that could play a role as a catalyst for the learning process. This paper presents an investigation into the use of the Twitter social media platform by selected top universities in UK. Twitter data from that account in the 1-year period was captured. First was coded, the total number of tweets, like ranking, usable (non-spam) tweets, the number of retweeted, hashtags and tweets on the official Twitter accounts of selected universities. In this study, NodeXL program was visualized and analyzed by drawing the data from Twitter. As such data sets of no more than 2,500 tweets were gathered for each search topic. After 60 years of experience with computer-based text analysis approaches can be used to define rule-based classification, theme extraction, ontology/taxonomy modeling, topic categorization and document summarization. Statistics (degree and weighted degree, centrality statistics, network diameter, graph density, average path length) were then calculated for each node and for the network using the statistical module of NodeXL. The data were visualized using Fruchterman-Reingold and Harel-Koren Fast Multiscale algorithms as shown in the figures below. The implications of this finding are discussed.


scholarship. Journal of Computer-Mediated Communication. 2008;13:210–230.

Bourdieu P. The Forms of Capital. In: Richardson J, editor. Handbook of Theory and Research for Sociology of Education. New York: Greenwood Press; 1986.

Cela KL, Sicilia MÁ, Sánchez S. Social network analysis in e-learning environments: A preliminary systematic review. Educational Psychology Review. 2014;27(1): 219–246. DOI:10.1007/s10648-014-9276-0

Chang YJ, Chang YS, Hsu SY, Chen CH. Social Network Analysis to Blog-based Online Community. 2007 International Conference on Convergence Information Technology (ICCIT 2007). DOI:10.1109/iccit.2007.130

Everton SF. Disrupting Dark Networks (Structural Analysis in Social Sciences). UK: Cambridge University Press; 2013.

Halevi A. Theorizing Network-Centric Activity in Education. USA: University of South Carolina; 2011.

Hanneman R, Riddle M. A brief introduction to analyzing social network data. In Scott J, Carrington PJ, editors. The SAGE handbook of social network analysis. London: SAGE Publications Ltd; 2014. p. 331-339. DOI:10.4135/9781446294413.n23

Huang C, Wang S, Su J, Zhao P. A Social Network Analysis of Changes in China’s Education Policy Information Transmission System (1978–2013). Higher Education Policy. 2018. DOI:10.1057/s41307-018-0096-6

Garbrick A, Clariana R. The influence of email notifications in asynchronous discussion on interaction patterns using social network analysis. In Global Learn 2015. Berlin, Germany; 2015. p. 622–626.

Java A, Song X, Finin T, Tseng B. Why we Twitter: Understanding microblogging usage and communities. Paper presented at the 9th WebKDD and 1st SNAKDD 2007 Workshop on Web Mining and Social Network Analysis. CA: San Jose. 2007.

Jeon JW, Wang Y, Yeo GT. (2016). SNA Approach for Analyzing the Research Trend of International Port Competition. The Asian Journal of Shipping and Logistics. 2016;32(3):165–172. DOI:10.1016/j.ajsl.2016.09.005

Ji YA, Nam SJ, Kim HG, Lee J, Lee SK. Research topics and trends in medical education by social network analysis. BMC Medical Education. 2018;18(1). DOI:10.1186/s12909-018-1323

Junco R, Heiberger G, Loken E. (2010). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning. 2010;27(2):119–132. DOI:10.1111/j.1365-2729.2010.00387.x

Knokeve D, Yang S. Social Network Analysis. London: Sage Publications; 2008.

Kolaczyk ED, Csárdi G. Statistical Analysis of Network Data with R. UK: Springer; 2014. DOI:10.1007/978-1-4939-0983-4

Kwak H, Lee C, Park H, Moon S. What is Twitter, a social network or a news media? Proceedings of the 19th International Conference on World Wide Web - WWW ’10. 2010. DOI:10.1145/1772690.1772751

Lewis TG. Network Science: Theory and Applications. New Jersey: John Wiley& Sons; 2009.

Liu B. Sentiment Analysis and Subjectivity. University of Illinois at Chicago; 2010.

Mahajan P. Use of social networking in a linguistically and culturally rich India. The International Information & Library Review. 2009;41:129-136.

Newman MEJ. Networks: An Introduction. New York: Oxford University Press; 2010.

Pajntar B. Overview of algorithms for graph drawing. Conference on Data Mining and Data Warehouses. 2006.

Reingen PH, Burt RS. Structural Holes: The Social Structure of Competition. Journal of Marketing. 1994;58(1):152. DOI:10.2307/1252259

Ruane R, Koku EF. Social Network Analysis of Undergraduate Education Student Interaction in Online Peer Mentoring Settings. Journal of Online Learning and Teaching. 2014;10(4):577-589.

Smith MA, Lee R, Shneiderman B, Himelboim I. Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Center; 2014.

Stein CH, Bush EG, Ross RR, Ward M. Mine, Yours and Ours: A Configural Analysis of the Networks of Married Couples in Relation to Marital Satisfaction and Individual Well-Being. Journal of Social and Personal Relationships. 1992;9(3):365–383. DOI:10.1177/0265407592093003

Tobias R. Lifelong learning under a comprehensive national qualifications framework-rhetoric and reality. International Journal of Lifelong Education. 1999;18(2):110-118.

Wasserman S, Faust K. Social network analysis: Methods and applications (Vol. 8). UK: Cambridge university press; 1994.

Yu AY, Tian SW, Vogel D, Chi-Wai Kwok R. Can learning be virtually boosted? An investigation of online social networking impacts. Computers & Education. 2010;55(4):1494–1503. doi:10.1016/j.compedu.2010.06.015

Zhao Y, Zhu Q, Wu K. The development of social network analysis research in mainland China: A literature review perspective. In Proceedings of the 2011 iConference on - iConference ’11 (pp. 296–303). New York, New York, USA: ACM Press; 2011. DOI:10.1145/1940761.1940802

Zweck J. (2006). Adjacency matrices. [Internet]. 2006 [accessed 2018 Sept 17]. Available from: http://www.utdallas.edu/~jwz120030/Teaching/PastCoursesUMBC/ M221HS06/ProjectFiles/Adjacency.pdf.

How to Cite
Bakan, U., Bakan, U., & Kan, T. (2019). Understanding the Usage Characteristics of Twitter in the UK Universities: A Social Network Analysis (SNA) Approach. LUMEN Proceedings, 7(1), 42-58. https://doi.org/10.18662/lumproc.98