Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ASL6VQULZ" target="_blank" >RIV/00216208:11320/22:SL6VQULZ - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3501247.3531567" target="_blank" >https://doi.org/10.1145/3501247.3531567</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3501247.3531567" target="_blank" >10.1145/3501247.3531567</a>
Alternative languages
Result language
angličtina
Original language name
Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation
Original language description
Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
14th ACM Web Science Conference 2022
ISBN
978-1-4503-9191-7
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
219-227
Publisher name
Association for Computing Machinery
Place of publication
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Event location
New York, NY, USA
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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