Search (2 results, page 1 of 1)

  • × author_ss:"Vaughan, L."
  • × year_i:[2010 TO 2020}
  • × theme_ss:"Suchmaschinen"
  1. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.02 
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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
    Type
    a
  2. Vaughan, L.; Romero-Frías, E.: Web search volume as a predictor of academic fame : an exploration of Google trends (2014) 0.00 
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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.
    Type
    a

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