The Effects on Topic Familiarity on Online Search Behaviour and Use of Relevance Criteria

Lei, Wen; Ruthven, Ian; Borlund, Pia

doi:10.1007/11735106_40

Cited by 18 publications

(22 citation statements)

References 9 publications

(11 reference statements)

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“…HsiehYee, 1993;Kelly and Cool, 2002;Toms et al, 2007;Wen et al, 2006;White et al, 2009;Zhang et al, 2005;Wildemuth, 2004). Knowledge impacts search tactics and strategies.…”

Section: Level Of Knowledgementioning

confidence: 99%

Task and user effects on reading patterns in information search

Cole

Gwizdka

Liu

et al. 2011

Interacting with Computers

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a b s t r a c tWe report on an investigation into people's behaviors on information search tasks, specifically the relation between eye movement patterns and task characteristics. We conducted two independent user studies (n = 32 and n = 40), one with journalism tasks and the other with genomics tasks. The tasks were constructed to represent information needs of these two different users groups and to vary in several dimensions according to a task classification scheme. For each participant we classified eye gaze data to construct models of their reading patterns. The reading models were analyzed with respect to the effect of task types and Web page types on reading eye movement patterns. We report on relationships between tasks and individual reading behaviors at the task and page level. Specifically we show that transitions between scanning and reading behavior in eye movement patterns and the amount of text processed may be an implicit indicator of the current task type facets. This may be useful in building user and task models that can be useful in personalization of information systems and so address design demands driven by increasingly complex user actions with information systems. One of the contributions of this research is a new methodology to model information search behavior and investigate information acquisition and cognitive processing in interactive information tasks.

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“…HsiehYee, 1993;Kelly and Cool, 2002;Toms et al, 2007;Wen et al, 2006;White et al, 2009;Zhang et al, 2005;Wildemuth, 2004). Knowledge impacts search tactics and strategies.…”

Section: Level Of Knowledgementioning

confidence: 99%

Task and user effects on reading patterns in information search

Cole

Gwizdka

Liu

et al. 2011

Interacting with Computers

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“…Topic familiarity has previously been shown to influence the rating of relevance criteria [22], thus we chose questions so that that they covered a wide breadth of topics and so that it would be unlikely that the participants would know the answers. In total we asked eight questions: …”

Section: Methodsmentioning

confidence: 99%

This image smells good

Loumakis¹,

Stumpf

Grayson³

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Users are confronted with an overwhelming amount of web pages when they look for information on the Internet. Current search engines already aid the user in their information seeking tasks by providing textual results but adding images to results pages could further help the user in judging the relevance of a result. We investigated this problem from an Information Foraging perspective and we report on two empirical studies that focused on the information scent of images. Our results show that images have their own distinct "smell" which is not as strong as that of text. We also found that combining images and text cues leads to a stronger overall scent. Surprisingly, when images were added to search engine results pages, this did not lead our participants to behave significantly differently in terms of effectiveness or efficiency. Even when we added images that could confuse the participants' scent, this had no significantly detrimental impact on their behaviour. However, participants expressed a preference for results pages which included images. We discuss potential challenges and point to future research to ensure the success of adding images to textual results in search engine results pages.

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“…Search behavior and relevance judgments can also vary according to topic, but usually this is a result of variations in user related variables, such as how much a user knows about a particular topic (e.g., [134,291]), and corpus related variables, such as how many relevant documents are available about a particular topic (e.g., [135]). It is common in many IR and IIR evaluations to investigate performance and other dependent measures with respect to topic in a post-hoc fashion (i.e., at the end of the study), but it is uncommon to treat topic as an independent variable.…”

Section: Information Needs: Tasks and Topicsmentioning

confidence: 99%