Crystal, A.; Greenberg, J.: Relevance criteria identified by health information users during Web searches (2006)
0.01
0.009625921 = product of:
0.019251842 = sum of:
0.019251842 = product of:
0.038503684 = sum of:
0.038503684 = weight(_text_:systems in 5909) [ClassicSimilarity], result of:
0.038503684 = score(doc=5909,freq=4.0), product of:
0.16037072 = queryWeight, product of:
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.052184064 = queryNorm
0.24009174 = fieldWeight in 5909, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.0731742 = idf(docFreq=5561, maxDocs=44218)
0.0390625 = fieldNorm(doc=5909)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This article focuses on the relevance judgments made by health information users who use the Web. Health information users were conceptualized as motivated information users concerned about how an environmental issue affects their health. Users identified their own environmental health interests and conducted a Web search of a particular environmental health Web site. Users were asked to identify (by highlighting with a mouse) the criteria they use to assess relevance in both Web search engine surrogates and full-text Web documents. Content analysis of document criteria highlighted by users identified the criteria these users relied on most often. Key criteria identified included (in order of frequency of appearance) research, topic, scope, data, influence, affiliation, Web characteristics, and authority/ person. A power-law distribution of criteria was observed (a few criteria represented most of the highlighted regions, with a long tail of occasionally used criteria). Implications of this work are that information retrieval (IR) systems should be tailored in terms of users' tendencies to rely on certain document criteria, and that relevance research should combine methods to gather richer, contextualized data. Metadata for IR systems, such as that used in search engine surrogates, could be improved by taking into account actual usage of relevance criteria. Such metadata should be user-centered (based on data from users, as in this study) and contextappropriate (fit to users' situations and tasks).
