Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

14th ACM Web Science Conference 2022

ISBN

978-1-4503-9191-7

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

219-227

Název nakladatele

Association for Computing Machinery

Místo vydání

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Místo konání akce

New York, NY, USA

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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