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Abstract

Webometrics research methods can be effectively used to measure and analyze information on the web. One topic discussed vehemently online that could benefit from this type of analysis is vaccines. We carried out a study analyzing the web presence of both sides of this debate. We collected a variety of webometric data and analyzed the data both quantitatively and qualitatively. The study found far more anti- than pro-vaccine web domains. The anti and pro sides had similar web visibility as measured by the number of links coming from general websites and Tweets. However, the links to the pro domains were of higher quality measured by PageRank scores. The result from the qualitative content analysis confirmed this finding. The analysis of site ages revealed that the battle between the two sides had a long history and is still ongoing. The web scene was polarized with either pro or anti views and little neutral ground. The study suggests ways that professional information can be promoted more effectively on the web. The study demonstrates that webometrics analysis is effective in studying online information dissemination. This kind of analysis can be used to study not only health information but other information as well.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1285-1294

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Abstract

Numerous web co-link studies have analyzed a wide variety of websites ranging from those in the academic and business arena to those dealing with politics and governments. Such studies uncover rich information about these organizations. In recent years, however, there has been a dearth of co-link analysis, mainly due to the lack of sources from which co-link data can be collected directly. Although several commercial services such as Alexa provide inlink data, none provide co-link data. We propose a new approach to web co-link analysis that can alleviate this problem so that researchers can continue to mine the valuable information contained in co-link data. The proposed approach has two components: (a) generating co-link data from inlink data using a computer program; (b) analyzing co-link data at the site level in addition to the page level that previous co-link analyses have used. The site-level analysis has the potential of expanding co-link data sources. We tested this proposed approach by analyzing a group of websites focused on vaccination using Moz inlink data. We found that the approach is feasible, as we were able to generate co-link data from inlink data and analyze the co-link data with multidimensional scaling.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.6, S.820-831