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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.
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Details
| Item type | Conference or workshop item (UNSPECIFIED) | ||||
| ISBN | 978-1-4503-9186-3 | ||||
| Title of Book | CHIIR '22: Proceedings of the 2022 Conference on Human Information Interaction and Retrieval | ||||
| Publisher | Association for Computing Machinery | ||||
| Open Access Type | Assoc. of Comp. Machinery (ACM) | ||||
| Place of Publication | New York, United States | ||||
| Page Range | pp. 113-122 | ||||
| Date | 2022 | ||||
| Date of publication | 15 Feb 2023 06:22 | ||||
| Institutions | Languages 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 |
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| Keywords | Featured Snippets, Answer Module, Credibility, Web Search, Question Answering, Search Behaviour | ||||
| Dewey Decimal Classification | 000 Computer science, information & general works > 000 Generalities, Science 000 Computer science, information & general works > 004 Computer science | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Partially | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-537618 | ||||
| Item ID | 53761 |
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