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Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior
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In: Boise State University Theses and Dissertations (2021)
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Abstract:
Online search engines for children are known to filter retrieved resources based on page complexity, and offer specialized functionality meant to address gaps in search literacy according to a user's age or grade. However, not every searcher grouped by these identifiers displays the same level of text comprehension, or requires the same aid with search. Furthermore, these search engines typically rely on direct feedback to ascertain these identifiers. This reliance on self identification may cause users to accidentally misrepresent themselves. We therefore seek to recognize users from skill based signals rather than utilizing age or grade identifiers, as skill dictates appropriate aid and resources. Therefore, in this thesis we propose a strategy that automatically recognizes users on the fly by analyzing search behavior found in search sessions. In particular, our efforts focus on recognizing the stereotypical 8 to 12 year old searcher, who we posit exhibits skills defined by developmental stages that have a strong impact on language development (Piaget's concrete operational stage) and search literacy (digital competency's first level). This strategy analyzes user-generated text extracted from queries and patterns of search interactions in order to infer features that are leveraged by a random forest classifier in order to determine whether or not a user is a part of this specific segment of searchers. The outcomes from this thesis lay the groundwork for enabling search engines to recognize users based on their search skills and provides further insight into the search behavior of youths.
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Keyword:
children; Computer Sciences; developmental stages; information retrieval; search engines; stereotyping; user segementation
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URL: https://scholarworks.boisestate.edu/td/1858
https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=2991&context=td
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| 2 |
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm ...
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| 3 |
Data set of the article: Language Bias in the Google Scholar Ranking Algorithm ...
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| 4 |
An Imprecise but Infinite Fall: The Machine Performance of Floating-Point Arithmetic
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In: Proceedings; Volume 81; Issue 1; Pages: 16 (2021)
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| 5 |
Awareness and Utilization of Search Engines for Information Retrieval by Students of National Open University of Nigeria in Enugu Study Centre Library
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In: Library Philosophy and Practice (e-journal) (2020)
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| 6 |
Contextual citation recommendation using scientific discourse annotation schemes
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| 7 |
Assessing the Robustness of Conversational Agents using Paraphrases
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| 8 |
Google and advertising: digital capitalism in the context of Post-Fordism, the reification of language, and the rise of fake news
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| 10 |
A dataset for open event extraction in English
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In: 10th International Conference on Language Resources and Evaluation, LREC 2016 ; https://hal-cea.archives-ouvertes.fr/cea-01843179 ; 10th International Conference on Language Resources and Evaluation, LREC 2016, May 2016, Portoroz, Slovenia. pp.1939-1943 (2016)
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| 11 |
ANALYSIS OF DOCUMENTS WHICH OBTAINED BY NETWORK ... : АНАЛИЗ ДОКУМЕНТОВ, ПОЛУЧЕННЫХ ИЗ СЕТИ INTERNET ...
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| 12 |
Applying data-driven learning to the web
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In: Multiple Affordances of Language Corpora for Data-driven Learning ; https://hal.archives-ouvertes.fr/hal-00937993 ; Agnieszka Lenko-Szymanska & Alex Boulton. Multiple Affordances of Language Corpora for Data-driven Learning, John Benjamins, pp.267-295, 2015, ⟨10.1075/scl.69.13bou⟩ (2015)
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| 13 |
Analysing entity context in multilingual wikipedia to support entity-centric retrieval applications ...
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| 14 |
DEEP-CARVING : Discovering Visual Attributes by Carving Deep Neural Nets ...
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| 15 |
DEEP-CARVING : Discovering Visual Attributes by Carving Deep Neural Nets
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| 16 |
Towards a Simple and Efficient Web Search Framework
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In: DTIC (2014)
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| 17 |
Building specialized bilingual lexicons using large-scale background knowledge
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In: 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 ; https://hal-cea.archives-ouvertes.fr/cea-01844695 ; 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, Oct 2013, Seattle, United States. pp.479-489 (2013)
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| 18 |
Semantically-enhanced image tagging system
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Rahuma, Awatef. - : De Montfort University, 2013. : Faculty of Technology, 2013. : Software Technology Research Laboratory, 2013
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Effectively and Efficiently Supporting Crowd-enabled Databases via NoSQL Paradigms
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| 20 |
Концептуальные вопросы проектирования конструктивных схем турбонасосных агрегатов двигателей летательных аппаратов
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БЕЛОУСОВ А.И.. - : Государственное образовательное учреждение высшего профессионального образования «Самарский государственный аэрокосмический университет им. академика С.П. Королёва (национальный исследовательский университет)», 2012
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