Relevance judgment in epistemic and hedonic information searches

Xu, Yunjie

doi:10.1002/asi.20461

Cited by 25 publications

(19 citation statements)

References 34 publications

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“…• Xu (2007) asked 113 undergraduate students to search the Web for documents of personal interest for self-education or relaxation and then choose and evaluate two documents that they have browsed; thus, analyses included 226 evaluated documents. The study was done to test five hypotheses related to criteria for informative relevance (resulting from "epistemic information searches"-desire for knowledge) and affective relevance (resulting from "hedonic information searches"-information for fun or affective stimulation) as opposed to situational relevance (resulting from problemoriented searches).…”

Section: Beyond Topical Do People Infer Relevance Based On Topicalitmentioning

confidence: 99%

Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance

Saračević

2007

J. Am. Soc. Inf. Sci.

210

214

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Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years or so and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized in two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The manifestations of relevance are classified as to several kinds of relevance that form an interdependent system of relevancies. In Part III, relevance behavior and effects are synthesized using experimental and observational works that incorporated data. In both parts, each section concludes with a summary that in effect provides an interpretation and synthesis of contemporary thinking on the topic treated or suggests hypotheses for future research. Analyses of some of the major trends that shape relevance work are offered in conclusions. Prologue to Part III: How It Is Connected and What This Work Is All AboutTo provide a continuation from the preceding Part II, a few basic descriptions about this work are repeated.As stated in the Preface to Part II, in 1975 I published a review about relevance under the same title, without, of course, "Part I" in the title (Saracevic, 1975). There was no plan then to have another related review 30 years later-but things happen. The intent of the 1975 work was "to explore the meaning of relevance as it has evolved in information science and to provide a framework within which various interpretations of relevance can be related" (Saracevic, 1975, p. 321).Building on the examination of relevance in the preceding (1975) review, this work (2007) follows the travails of relevance in information science for the past 30 years. It is an update. The aim of this work is still substantially the same: It is an attempt to trace the evolution of thinking on relevance in information science for the past three decades and to provide an updated, contemporary framework within which the still widely dissonant ideas on relevance might be interpreted and related to one another.The organization of the present review, offered in two parts, addresses the questions related to relevance nature, manifestations, behavior, and effects. Following the Introduction and a Historical Footnote, the preceding Part II (this issue, pp. 1915-1933) started with a general section on the nature of relevance by synthesizing its meanings, following with sections on theories and models of relevance that are, in effect, further elaborations on the nature of relevance. Part ...

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Section: Beyond Topical Do People Infer Relevance Based On Topicalitmentioning

confidence: 99%

Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance

Saračević

2007

J. Am. Soc. Inf. Sci.

210

214

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“…In addition, "interesting" content is not simply that which provides positive enjoyment, since content that conveys anger or frustration may also be of interest. Therefore we consider interesting content to be that giving rise to significant affective stimulation [12] for some group of users. An individual who retweets content is signalling that they believe it will have a level of affective stimulation for their followers.…”

Section: Related Workmentioning

confidence: 99%

“…We assess interestingness for a tweet by considering the extent to which it has provided affective stimulation [12] the group of users that have encountered it in their timeline. The signal we use for affective stimulation is a retweet.…”

Section: Inferring Interestingnessmentioning

confidence: 99%

Retweeting beyond expectation: Inferring interestingness in Twitter

Webberley

Allen

Whitaker

2016

Computer Communications

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a b s t r a c tOnline social networks such as Twitter have emerged as an important mechanism for individuals to share information and post user generated content. However, filtering interesting content from the large volume of messages received through Twitter places a significant cognitive burden on users. Motivated by this problem, we develop a new automated mechanism to detect personalised interestingness, and investigate this for Twitter. Instead of undertaking semantic content analysis and matching of tweets, our approach considers the human response to content, in terms of whether the content is sufficiently stimulating to get repeatedly chosen by users for forwarding (retweeting). This approach involves machine learning against features that are relevant to a particular user and their network, to obtain an expected level of retweeting for a user and a tweet. Tweets observed to be above this expected level are classified as interesting. We implement the approach in Twitter and evaluate it using comparative human tweet assessment in two forms: through aggregated assessment using Mechanical Turk, and through a web-based experiment for Twitter users. The results provide confidence that the approach is effective in identifying the more interesting tweets from a user's timeline. This has important implications for reduction of cognitive burden: the results show that timelines can be considerably shortened while maintaining a high degree of confidence that more interesting tweets will be retained. In conclusion we discuss how the technique could be applied to mitigate possible filter bubble effects.

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“…In this study, relevance is restricted to cognitive and situational relevance of a document. This study is not interested in affective relevance (Xu, 2007a) or its social implications.…”

Section: Relevancementioning

confidence: 99%

“…There are many other criteria involved in a relevance judgment such as the reliability, recency, novelty, understandability, and scope of a document. The set of significant criteria might change to different contexts and users; nevertheless, there is core set of criteria that remains stable across situations (Saracevic, 2007;Xu & Chen, 2006;Xu, 2007a). Based on Grice's (1989) communication theory, Xu and Chen (2006) have summarized a vast number of variables into a small set of five key relevance criteria: topicality, novelty, reliability, understandability, and scope.…”

Section: Relevancementioning

confidence: 99%

Order effect in relevance judgment

Wang

2008

J. Am. Soc. Inf. Sci.

Self Cite

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The order effect of relevance judgment refers to the different relevance perceptions of a document when it appears in different positions in a list. Although the order effect of relevance judgment has significant theoretical and practical implications, the extant literature is inconclusive regarding its existence and forming mechanisms. This study proposes a set of order effect forming mechanisms, including the learning effect, the subneed scheduling effect, and the cursoriness effect based on the conceptualization of dynamic relevance and the psychology of cognitive elaboration. Our empirical study indicates that in an interactive information retrieval setting, when a document list is reasonably long, order effects demonstrate a curvilinear pattern that conforms to the combined effect of the three mechanisms. Moreover, the curvilinear pattern of order effect could differ for documents of different relevance levels.

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Relevance judgment in epistemic and hedonic information searches

Cited by 25 publications

References 34 publications

Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance

Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance

Retweeting beyond expectation: Inferring interestingness in Twitter

Order effect in relevance judgment

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