View in EDS

What identifies different age cohorts in Yahoo! Answers?

Saved in:
Bibliographic Details
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
Description
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]
ISSN:09507051
DOI:10.1016/j.knosys.2021.107278