Search (10 results, page 1 of 1)

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Abstract

Commercial search engines are now playing an increasingly important role in Web information dissemination and access. Of particular interest to business and national governments is whether the big engines have coverage biased towards the US or other countries. In our study we tested for national biases in three major search engines and found significant differences in their coverage of commercial Web sites. The US sites were much better covered than the others in the study: sites from China, Taiwan and Singapore. We then examined the possible technical causes of the differences and found that the language of a site does not affect its coverage by search engines. However, the visibility of a site, measured by the number of links to it, affects its chance to be covered by search engines. We conclude that the coverage bias does exist but this is due not to deliberate choices of the search engines but occurs as a natural result of cumulative advantage effects of US sites on the Web. Nevertheless, the bias remains a cause for international concern.

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Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

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Abstract

A set of measurements is proposed for evaluating Web search engine performance. Some measurements are adapted from the concepts of recall and precision, which are commonly used in evaluating traditional information retrieval systems. Others are newly developed to evaluate search engine stability, an issue unique to Web information retrieval systems. An experiment was conducted to test these new measurements by applying them to a performance comparison of three commercial search engines: Google, AltaVista, and Teoma. Twenty-four subjects ranked four sets of Web pages and their rankings were used as benchmarks against which to compare search engine performance. Results show that the proposed measurements are able to distinguish search engine performance very well.

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Abstract

Searches conducted on web search engines reflect the interests of users and society. Google Trends, which provides information about the queries searched by users of the Google web search engine, is a rich data source from which a wealth of information can be mined. We investigated the possibility of using web search volume data from Google Trends to predict academic fame. As queries are language-dependent, we studied universities from two countries with different languages, the United States and Spain. We found a significant correlation between the search volume of a university name and the university's academic reputation or fame. We also examined the effect of some Google Trends features, namely, limiting the search to a specific country or topic category on the search volume data. Finally, we examined the effect of university sizes on the correlations found to gain a deeper understanding of the nature of the relationships.

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Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.

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Abstract

Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.

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Abstract

The number and type of Web citations to journal articles in four areas of science are examined: biology, genetics, medicine, and multidisciplinary sciences. For a sample of 5,972 articles published in 114 journals, the median Web citation counts per journal article range from 6.2 in medicine to 10.4 in genetics. About 30% of Web citations in each area indicate intellectual impact (citations from articles or class readings, in contrast to citations from bibliographic services or the author's or journal's home page). Journals receiving more Web citations also have higher percentages of citations indicating intellectual impact. There is significant correlation between the number of citations reported in the databases from the Institute for Scientific Information (ISI, now Thomson Scientific) and the number of citations retrieved using the Google search engine (Web citations). The correlation is much weaker for journals published outside the United Kingdom or United States and for multidisciplinary journals. Web citation numbers are higher than ISI citation counts, suggesting that Web searches might be conducted for an earlier or a more fine-grained assessment of an article's impact. The Web-evident impact of non-UK/USA publications might provide a balance to the geographic or cultural biases observed in ISI's data, although the stability of Web citation counts is debatable.

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Abstract

Vaughn, and Shaw look at the relationship between traditional citation and Web citation (not hyperlinks but rather textual mentions of published papers). Using English language research journals in ISI's 2000 Journal Citation Report - Information and Library Science category - 1209 full length papers published in 1997 in 46 journals were identified. Each was searched in Social Science Citation Index and on the Web using Google phrase search by entering the title in quotation marks, and followed for distinction where necessary with sub-titles, author's names, and journal title words. After removing obvious false drops, the number of web sites was recorded for comparison with the SSCI counts. A second sample from 1992 was also collected for examination. There were a total of 16,371 web citations to the selected papers. The top and bottom ranked four journals were then examined and every third citation to every third paper was selected and classified as to source type, domain, and country of origin. Web counts are much higher than ISI citation counts. Of the 46 journals from 1997, 26 demonstrated a significant correlation between Web and traditional citation counts, and 11 of the 15 in the 1992 sample also showed significant correlation. Journal impact factor in 1998 and 1999 correlated significantly with average Web citations per journal in the 1997 data, but at a low level. Thirty percent of web citations come from other papers posted on the web, and 30percent from listings of web based bibliographic services, while twelve percent come from class reading lists. High web citation journals often have web accessible tables of content.

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Abstract

Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of these data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This article discusses the difference between a symmetrical cocitation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (such as the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical cocitation matrix but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment, in which the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed by using both the traditional methods of multivariate analysis and the new visualization software Pajek, which is based on social network analysis and graph theory.

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Abstract

Earlier studies found that web hyperlink data contain various types of information, ranging from academic to political, that can be used to analyze a variety of social phenomena. Specifically, the numbers of inlinks to academic websites are associated with academic performance, while the counts of inlinks to company websites correlate with business variables. However, the scarcity of sources from which to collect inlink data in recent years has required us to seek new data sources. The recent demise of the inlink search function of Yahoo! made this need more pressing. Different alternative variables or data sources have been proposed. This study compared three types of web data to determine which are better as academic and business quality estimates, and what are the relationships among the three data sources. The study found that Alexa inlink and Google URL citation data can replace Yahoo! inlink data and that the former is better than the latter. Alexa is even better than Yahoo!, which has been the main data source in recent years. The unique nature of Alexa data could explain its relative advantages over other data sources.