Information Scent, Searching and Stopping

Maxwell, David; Azzopardi, Leif

doi:10.1007/978-3-319-76941-7_16

Cited by 13 publications

(17 citation statements)

References 36 publications

(54 reference statements)

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“…Information scent is a term derived from the information foraging theory, which explains human information-seeking and sense interface. Information scent refers to information seekers following hints as a form of either visual or textual context in search of the desired information [23,29,30]. A strong information scent can convince users that they will find what they are looking for at the end of the journey.…”

Section: Methodsmentioning

confidence: 99%

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Roosan¹,

Yan²,

Law³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundDespite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.ObjectiveThe objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype.MethodsTwo pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted.ResultsA mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations.ConclusionsUsing HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype.

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Section: Methodsmentioning

confidence: 99%

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Roosan¹,

Yan²,

Law³

et al. 2019

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…In another body of work, Wu et al [20] and others [4,14,15] used information forging theory to better understand how users seek information in the web. Wu et al [20] manipulated the number of the relevant documents in the search results of users first 3 queries', and asked users to search for relevant documents to open-ended question.…”

Section: And Efthimiadismentioning

confidence: 99%

“…• Unlike Joachims et al [10] and others [14,15,20] where many of their search tasks include multiple relevant documents in the SERP, our focus is not in investigating which document among those that are relevant should the user click at, but on understanding possible causes to how far are users are willing to examine SERPs with either no relevant document or one relevant document placed at different ranks and what motivates users to continue or stop their examination. We aim to understand how different reasons to query abandonment can affect examination and vice-versa, i.e.…”

Section: And Efthimiadismentioning

confidence: 99%

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

2019

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To determine key factors that affect a user's behavior with search results, we conducted a controlled eye-tracking study of users completing search tasks using both desktop and mobile devices. We focus our investigation on users' behavior from their query to the first action they take with the search engine results page (SERP): either a click on a search result or a reformulation of their query. We found that a user deciding to reformulate a query rather than click on a result is best understood as being caused by the user's examination pattern not including a relevant search result. If a user sees a relevant result, they are very likely to click it. Of note, users do not look at all search results and their examination may be influenced by other factors. The key factors we found to explain a user's examination pattern are: the rank of search results, the user type, and the query quality. While existing research has identified rank and user types as important factors affecting examination patterns, to our knowledge, query quality is a new discovery. We found that user queries can be understood as either of weak or strong quality. Weak queries are those that the user may believe are more likely to fail compared to a strong query, and as a result, we find that users modify their examination patterns to view fewer documents when they issue a weak query, i.e. they give up sooner.

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“…Also, users often apply several rounds of search to reformulate their queries further to adhere to their information needs given they find some relevant results. Past work [6,20] demonstrated the use of information scent to model users' information need during web search, and it has been used to understand the factors affecting search and what takes a user to stop the search. Despite the good observation, the exploitation of information scent (from Information Foraging Theory [27]) is under-explored in case of ambiguous queries and have not been extended to take into account an image in query expansion (or suggestion) tasks.…”

Section: Introductionmentioning

confidence: 99%

Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Jaiswal

Liu

Frommholz

2020

Lecture Notes in Computer Science

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Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user's information need (IN) context. In this work, we devise a new task of QAC applied on an image for estimating patch (one of the key components of Information Foraging Theory) probabilities for query suggestion. Our work supports query completion by extending a user query prefix (one or two characters) to a complete query utilising a foraging-based probabilistic patch selection model. We present iBERT, to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model, which leverages combined textual-image queries for a solution to image QAC by computing probabilities of a large set of image patches. The reflected patch probabilities are used for selection while being agnostic to changing information need or contextual mechanisms. Experimental results show that query autocompletion using both natural language queries and images is more effective than using only language-level queries. Also, our fine-tuned iBERT model allows to efficiently rank patches in the image.

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Information Scent, Searching and Stopping

Cited by 13 publications

References 36 publications

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

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