Hyperlinks embedded in Twitter as a proxy for total external inlinks to international university websites

TL;DR

This study explores Twitter's potential as an alternative data source for webometric analysis of university websites by examining the correlation between embedded hyperlinks in tweets and traditional link indicators.

Contribution

It demonstrates that Twitter data, via Topsy, correlates significantly with established link metrics, offering a promising supplementary approach for webometric studies.

Findings

High correlation between Twitter hyperlinks and link indicators (r=0.769).

Twitter data shows skewness influenced by specific universities and countries.

Prediction models have high error rates, especially in nonlinear environments.

Abstract

This article analyzes Twitter as a potential alternative source of external links for use in webometric analysis because of its capacity to embed hyperlinks in different tweets. Given the limitations on searching Twitter's public API, we decided to use 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 ability to predict external links from the collected tweets. The results indicate a high volume of tweets, although they are skewed by the presence and 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, which fall slightly in nonlinear models applied to specific environments. 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.