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Gender classification of product reviewers in China: a data-driven approach.

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Title: Gender classification of product reviewers in China: a data-driven approach.
Authors: Wang, Jing1 (AUTHOR) jingwang@cuc.edu.cn, Yan, Xiangbin2 (AUTHOR) xbyan@ustb.edu.cn, Zhu, Bin3 (AUTHOR) Bin.Zhu@bus.oregonstate.edu
Source: Information Technology & Management. Mar2026, Vol. 27 Issue 1, p113-124. 12p.
Subjects: Gender, Internet forums, Marketing, Product reviews, Discourse analysis, Consumer preferences, Chinese people, Quantitative research
Geographic Terms: China
Abstract: Online product discussion forums have become essential resources for marketers seeking to understand market dynamics and consumer preferences. Identifying the gender of forum participants can further enhance the effectiveness and efficiency of marketing efforts. However, the relationship between linguistic features and gender classification often varies due to contextual factors such as genres, social networks, and social classes. Recognizing that the discriminatory power of gender markers changes with context, this study proposes and validates a framework to guide the adoption of existing gender classification systems specifically for online product discussions. We demonstrate that beyond optimizing the classification methods themselves, performance can be improved by strategically applying these methods to archived discussion data. Our findings reveal that, for a given classification method and discussion forum, the size of the input data significantly influences performance, with an optimal data size existing to achieve the best results. [ABSTRACT FROM AUTHOR]
Copyright of Information Technology & Management is the property of Springer Nature 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
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  Data: Gender classification of product reviewers in China: a data-driven approach.
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  Data: <searchLink fieldCode="JN" term="%22Information+Technology+%26+Management%22">Information Technology & Management</searchLink>. Mar2026, Vol. 27 Issue 1, p113-124. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Gender%22">Gender</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+forums%22">Internet forums</searchLink><br /><searchLink fieldCode="DE" term="%22Marketing%22">Marketing</searchLink><br /><searchLink fieldCode="DE" term="%22Product+reviews%22">Product reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Discourse+analysis%22">Discourse analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+people%22">Chinese people</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink>
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  Data: Online product discussion forums have become essential resources for marketers seeking to understand market dynamics and consumer preferences. Identifying the gender of forum participants can further enhance the effectiveness and efficiency of marketing efforts. However, the relationship between linguistic features and gender classification often varies due to contextual factors such as genres, social networks, and social classes. Recognizing that the discriminatory power of gender markers changes with context, this study proposes and validates a framework to guide the adoption of existing gender classification systems specifically for online product discussions. We demonstrate that beyond optimizing the classification methods themselves, performance can be improved by strategically applying these methods to archived discussion data. Our findings reveal that, for a given classification method and discussion forum, the size of the input data significantly influences performance, with an optimal data size existing to achieve the best results. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Technology & Management is the property of Springer Nature 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.)
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        Value: 10.1007/s10799-024-00443-0
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        Text: English
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      – SubjectFull: Discourse analysis
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      – SubjectFull: Consumer preferences
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      – SubjectFull: Chinese people
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      – SubjectFull: China
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      – TitleFull: Gender classification of product reviewers in China: a data-driven approach.
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              Text: Mar2026
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