What identifies different age cohorts in Yahoo! Answers?
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| Title: | What identifies different age cohorts in Yahoo! Answers? |
|---|---|
| Authors: | Figueroa, Alejandro1 (AUTHOR) alejandro.figueroa@unab.cl, Timilsina, Mohan2 (AUTHOR) mohan.timilsina@insight-centre.org |
| Source: | Knowledge-Based Systems. Sep2021, Vol. 228, pN.PAG-N.PAG. 1p. |
| Subjects: | Virtual communities, Age groups, Metadata, Conformance testing, User experience, Natural language processing |
| Abstract: | For different kinds of online platforms, understanding demographics has shown to be instrumental in improving user experience, especially for personalizing and contextualizing content. Needless to say, there has been a number of studies delving into demographics in online social media platforms including Facebook and Twitter. However, only a mere handful of works have explored demographic factors behind community question-answering platforms despite their massive amount of members. For this reason, we decided to undertake a study of Yahoo! Answers members, namely as it relates to age demographics. To this end, we automatically built and annotated a large-scale corpus comprising metadata and textual inputs produced by ca. 650,000 community fellows. We profit from this collection by conducting both an exploratory/statistical analysis and predictive modelling. In the former, we explored the correlation between distinct age groups and some variables that, intuitively, can seem to be highly correlated with some cohorts. Interestingly enough, this analysis revealed that Millennials are answering questions prompted by their succeeding age group (GEN Z). In the latter, we assessed the prediction rate of various traditional statistical methods and neural networks classifiers coupled with numerous combinations of assorted textual and metadata features. Overall, best classifiers finished with an MRR of up to 0.862, and were modelled by means of FastText and Maximum Entropy (MaxEnt). In terms of informative attributes, user asking/answering activity patterns and sentimentally charged words provide telltale clues about which age group a community peer belongs to. • Large-scale exploratory and predictive analysis of age cohorts across cQA platforms. • Numerous types of approaches tested coupled with assorted features. • Millennials are answering questions prompted by their succeeding age group (GEN Z). • User activity and sentimentally charged words provide telltale clues about age groups. • Effective models for age cohorts and question intent are strikingly similar. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge-Based Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 151856018 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: What identifies different age cohorts in Yahoo! Answers? – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Figueroa%2C+Alejandro%22">Figueroa, Alejandro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alejandro.figueroa@unab.cl</i><br /><searchLink fieldCode="AR" term="%22Timilsina%2C+Mohan%22">Timilsina, Mohan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mohan.timilsina@insight-centre.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge-Based+Systems%22">Knowledge-Based Systems</searchLink>. Sep2021, Vol. 228, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Virtual+communities%22">Virtual communities</searchLink><br /><searchLink fieldCode="DE" term="%22Age+groups%22">Age groups</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Conformance+testing%22">Conformance testing</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: For different kinds of online platforms, understanding demographics has shown to be instrumental in improving user experience, especially for personalizing and contextualizing content. Needless to say, there has been a number of studies delving into demographics in online social media platforms including Facebook and Twitter. However, only a mere handful of works have explored demographic factors behind community question-answering platforms despite their massive amount of members. For this reason, we decided to undertake a study of Yahoo! Answers members, namely as it relates to age demographics. To this end, we automatically built and annotated a large-scale corpus comprising metadata and textual inputs produced by ca. 650,000 community fellows. We profit from this collection by conducting both an exploratory/statistical analysis and predictive modelling. In the former, we explored the correlation between distinct age groups and some variables that, intuitively, can seem to be highly correlated with some cohorts. Interestingly enough, this analysis revealed that Millennials are answering questions prompted by their succeeding age group (GEN Z). In the latter, we assessed the prediction rate of various traditional statistical methods and neural networks classifiers coupled with numerous combinations of assorted textual and metadata features. Overall, best classifiers finished with an MRR of up to 0.862, and were modelled by means of FastText and Maximum Entropy (MaxEnt). In terms of informative attributes, user asking/answering activity patterns and sentimentally charged words provide telltale clues about which age group a community peer belongs to. • Large-scale exploratory and predictive analysis of age cohorts across cQA platforms. • Numerous types of approaches tested coupled with assorted features. • Millennials are answering questions prompted by their succeeding age group (GEN Z). • User activity and sentimentally charged words provide telltale clues about age groups. • Effective models for age cohorts and question intent are strikingly similar. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge-Based Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=151856018 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.knosys.2021.107278 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Virtual communities Type: general – SubjectFull: Age groups Type: general – SubjectFull: Metadata Type: general – SubjectFull: Conformance testing Type: general – SubjectFull: User experience Type: general – SubjectFull: Natural language processing Type: general Titles: – TitleFull: What identifies different age cohorts in Yahoo! Answers? Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Figueroa, Alejandro – PersonEntity: Name: NameFull: Timilsina, Mohan IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 09 Text: Sep2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 09507051 Numbering: – Type: volume Value: 228 Titles: – TitleFull: Knowledge-Based Systems Type: main |
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