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Bink, Markus ; Zimmerman, Steven ; Elsweiler, David

Featured Snippets and their Influence on Users’ Credibility Judgements

Conference or workshop item

Bink, Markus, Zimmerman, Steven and Elsweiler, David (2022) Featured Snippets and their Influence on Users’ Credibility Judgements. In: CHIIR '22: ACM SIGIR Conference on Human Information Interaction and Retrieval, March 14 - 18, 2022, Regensburg Germany.


Abstract

Search engines often provide featured snippets, which are boxed and placed above other results with the aim of directly answering user queries. To learn about how users judge the credibility of such results and how they influence search outcomes, a controlled web-based user study (N = 96) was conducted. Using resources made available by scholars in the community, we study featured snippets in a ...

Search engines often provide featured snippets, which are boxed and placed above other results with the aim of directly answering user queries. To learn about how users judge the credibility of such results and how they influence search outcomes, a controlled web-based user study (N = 96) was conducted. Using resources made available by scholars in the community, we study featured snippets in a medical context with participants being tasked with determining whether a named treatment is helpful for a specified medical condition both before and after viewing the search results. Experimental conditions varied the presence and credibility of featured snippets. Our findings indicate that participants tend to overestimate the credibility of information in featured snippets. Featured snippets are, moreover, shown to often change users’ opinion about a topic, especially if they are uncertain. Showing correct information inside featured snippets helped participants make more accurate decisions, whereas incorrect or contradicting information led to more harmful outcomes.



Involved Institutions


Details

Item typeConference or workshop item (UNSPECIFIED)
ISBN978-1-4503-9186-3
Title of BookCHIIR '22: Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
PublisherAssociation for Computing Machinery
Open Access TypeAssoc. of Comp. Machinery (ACM)
Place of PublicationNew York, United States
Page Rangepp. 113-122
Date2022
Date of publication15 Feb 2023 06:22
InstitutionsLanguages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz)
Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz)
Identification Number
ValueType
10.1145/3498366.3505766DOI
KeywordsFeatured Snippets, Answer Module, Credibility, Web Search, Question Answering, Search Behaviour
Dewey Decimal Classification000 Computer science, information & general works > 000 Generalities, Science
000 Computer science, information & general works > 004 Computer science
StatusPublished
RefereedYes, this version has been refereed
Created at the University of RegensburgPartially
URN of the UB Regensburgurn:nbn:de:bvb:355-epub-537618
Item ID53761

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