Hyperlinks embedded in twitter as a proxy for total external in‐links to international university websites

Abstract

Twitter as a potential alternative source of external links for use in webometric analysis is analyzed because of its capacity to embed hyperlinks in different tweets. Given the limitations on searching Twitter's public application programming interface (API), we used the Topsy search engine as a source for compiling tweets. To this end, we took a global sample of 200 universities and compiled all the tweets with hyperlinks to any of these institutions. Further link data was obtained from alternative sources (MajesticSEO and OpenSiteExplorer) in order to compare the results. Thereafter, various statistical tests were performed to determine the correlation between the indicators and the possibility of predicting external links from the collected tweets. The results indicate a high volume of tweets, although they are skewed by the performance of specific universities and countries. The data provided by Topsy correlated significantly with all link indicators, particularly with OpenSiteExplorer (r = 0.769). Finally, prediction models do not provide optimum results because of high error rates. We conclude that the use of Twitter (via Topsy) as a source of hyperlinks to universities produces promising results due to its high correlation with link indicators, though limited by policies and culture regarding use and presence in social networks.

A new approach to web co‐link analysis

“…Orduña‐Malea and Aytac (2015) used inlink data from Open Site Explorer in their analysis of the relationship between Turkish academic and industry systems. Orduña‐Malea and Regazzi () and Orduña‐Malea, Torres‐Salinas, and López‐Cózar () collected inlink data from both Majestic SEO and Open Site Explorer in their studies of U.S. academic libraries and international universities, respectively. All these three services (Alexa, Moz, and Majestic) provide inlink data but not co‐link data.…”

Web citations in patents: Evidence of technological impact?

“…An alternative to using web link variants is to use social media metrics such as commenting, downloading, and recommending, known as altmetrics (Priem & Hemminger, ; Thelwall, 2012). Some of these metrics include hyperlinks, for example within Tweets (Orduna‐Malea, Torres‐Salinas, & Delgado López‐Cózar, ; Vaughan, ), but most focus on impact indicators for individual articles or journals (Haustein & Siebenlist, ) rather than for entire organizations. The number of links from Twitter (Orduna‐Malea et al, ) and Wikipedia (Orduna‐Malea & Ontalba‐Ruipérez, ) correlate with the inlinks received by sets of national and international universities, giving evidence of their value.…”

Measuring visibility of disciplines on Chinese academic web

“…A few previous studies have been implemented to analyse the macro-structure of universities within a country or between countries, most of which focused on geographic clustering or topological indicators [15,18,20,21,25]. Although the comprehensive influence of institutions or countries on the web has been widely analysed [14,21,92], only a very limited number of the studies discuss the disciplinary relationships between academic institutions intuitively, and the discipline structure has not been thoroughly analysed and evaluated in terms of some measurable indicators. Our study is specifically designed to explore the clustering phenomenon of schools and universities on the web, and then to measure the discipline visibility quantitatively.…”

“…In this study, we do not compare the disciplinary visibility between the publication-based networks and the hyperlink network at the school level because of instability of school names caused by frequent internal affiliation mergers and various translation of school names [70]. Social media may be another alternative to explore the relationships between universities [92] or to group online users [96,97]. Although the relationships between schools or universities on social media are not involved in this study, Vaughan [98] found that inlink data from social media are highly correlated with that from the general web.…”