Webometrics: evolution of social media presence of universities

This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005–2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012–2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005–2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.

[1]  Sophia Ananiadou,et al.  Identification of research hypotheses and new knowledge from scientific literature , 2018, BMC Medical Informatics and Decision Making.

[2]  Peter Ingwersen,et al.  Informetric analyses on the world wide web: methodological approaches to 'webometrics' , 1997, J. Documentation.

[3]  Peter Haddawy,et al.  A bibliometric study of research activity in ASEAN related to the EU in FP7 priority areas , 2012, Scientometrics.

[4]  W. Admiraal,et al.  Students' engagement in asynchronous online discussion: The relationship between cognitive presence, learner prominence, and academic performance , 2019, Internet High. Educ..

[5]  Rafael Anaya-Sánchez,et al.  Social media-based collaborative learning: Exploring antecedents of attitude , 2018, Internet High. Educ..

[6]  Saeed-Ul Hassan,et al.  Tapping into intra- and international collaborations of the Organization of Islamic Cooperation states across science and technology disciplines , 2016 .

[7]  Sarana Nutanong,et al.  An Effective and Scalable Framework for Authorship Attribution Query Processing , 2018, IEEE Access.

[8]  Peter Ingwersen,et al.  Toward a basic framework for webometrics , 2004, J. Assoc. Inf. Sci. Technol..

[9]  David Gunnarsson Lorentzen Webometrics benefitting from web mining? An investigation of methods and applications of two research fields , 2013, Scientometrics.

[10]  Isidro F. Aguillo,et al.  Webometric Ranking of World Universities: Introduction, Methodology, and Future Developments , 2008 .

[11]  Saeed-Ul Hassan,et al.  Scientific collaboration networks in Pakistan and their impact on institutional research performance , 2019, Libr. Hi Tech.

[12]  Peter Haddawy,et al.  Explaining the transatlantic gap in research excellence , 2016, Scientometrics.

[13]  Mike Thelwall,et al.  An investigation of the online presence of UK universities on Instagram , 2017, Online Inf. Rev..

[14]  Subal Chandra Biswas,et al.  Search Engines and Alternative Data Sources in Webometric Research: An Exploratory Study , 2015 .

[15]  M. Brown,et al.  Implementing online personalized social comparison nudges in a web-enabled coaching system , 2019, Internet High. Educ..

[16]  Francisco Herrera,et al.  Predicting literature's early impact with sentiment analysis in Twitter , 2020, Knowl. Based Syst..

[17]  Sarana Nutanong,et al.  The Key Factors and Their Influence in Authorship Attribution , 2016, Res. Comput. Sci..

[18]  Daniel T. Hickey,et al.  Internet-based alternatives for equitable preparation, access, and success in gateway courses , 2020, Internet High. Educ..

[19]  Peter Haddawy,et al.  Analyzing knowledge flows of scientific literature through semantic links: a case study in the field of energy , 2015, Scientometrics.

[20]  Qing Li,et al.  A scalable framework for cross-lingual authorship identification , 2018, Inf. Sci..

[21]  Sophia Ananiadou,et al.  Enriching news events with meta-knowledge information , 2016, Language Resources and Evaluation.

[22]  Saeed-Ul Hassan,et al.  A bibliometric perspective of learning analytics research landscape , 2018, Behav. Inf. Technol..

[23]  Saeed-Ul Hassan,et al.  A bibliometric assessment of scientific productivity and international collaboration of the Islamic World in science and technology (S&T) areas , 2015, Scientometrics.

[24]  Peter Haddawy,et al.  The solitude of stars. An analysis of the distributed excellence model of European universities , 2017, J. Informetrics.