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Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01

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Date: 22. 9.2008 18:31:54

Yi, K.; Beheshti, J.; Cole, C.; Leide, J.E.; Large, A.: User search behavior of domain-specific information retrieval systems : an analysis of the query logs from PsycINFO and ABC-Clio's Historical Abstracts/America: History and Life (2006) 0.01
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Abstract

The authors report the findings of a study that analyzes and compares the query logs of PsycINFO for psychology and the two history databases of ABC-Clio: Historical Abstracts and America: History and Life to establish the sociological nature of information need, searching, and seeking in history versus psychology. Two problems are addressed: (a) What level of query log analysis - by individual query terms, by co-occurrence of word pairs, or by multiword terms (MWTs) - best serves as data for categorizing the queries to these two subject-bound databases; and (b) how can the differences in the nature of the queries to history versus psychology databases aid in our understanding of user search behavior and the information needs of their respective users. The authors conclude that MWTs provide the most effective snapshot of user searching behavior for query categorization. The MWTs to ABC-Clio indicate specific instances of historical events, people, and regions, whereas the MWTs to PsycINFO indicate concepts roughly equivalent to descriptors used by PsycINFO's own classification scheme. The average length of queries is 3.16 terms for PsycINFO and 3.42 for ABC-Clio, which breaks from findings for other reference and scholarly search engine studies, bringing query length closer in line to findings for general Web search engines like Excite.
Yi, K.; Choi, N.; Kim, Y.S.: ¬A content analysis of Twitter hyperlinks and their application in web resource indexing (2016) 0.01
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Abstract

Twitter has emerged as a popular source of sharing and delivering news information. In tweet messages, URLs to web resources and hashtags are often included. This study investigates the potential of the hyperlinks and hashtags as topical clues and indicators to tweet messages. For this study, we crawled and analyzed about 1.5 million tweets for a 3-month period covering any topic or subject. The findings of this study revealed a power law relationship for the ranking and frequency of (a) the host names of URLs, and (b) a pair of hashtags and URLs that appeared in the tweet messages. This study also discovered that the most popular URLs used in tweets come from news and media websites, and a majority of the hyperlinked resources are news web pages. One implication of this study is that Twitter users are becoming more active in sharing already published information than producing new information. Finally, our investigation on hashtags for web resource indexing reveals that hashtags have the potential to be used as indexing terms for co-occurring URLs in the same tweet. We also discuss the implications of this study for web resource recommendation.

Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.01

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Date: 25.12.2012 15:22:37

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