Quotation Sikdar, Sandipan, Sachdeva, Rachneet, Wachs, Johannes, Lemmerich, Florian, Strohmaier, Markus. 2022. The Effects of Gender Signals and Performance in Online Product Reviews. Frontiers in Big Data. 4 (77140)




This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products. Differences in reviews, perceived—consciously or unconsciously—with respect to gender signals, can lead to crucial biases in determining what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names to select reviews where the author’s likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep learning classifier to quantify the gendered writing style (i.e., gendered performance) of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on the review helpfulness ratings using matching experiments. This is aimed at understanding if an advantage is to be gained by (not) signaling one’s gender when posting reviews. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, we believe this general chain of tools could be deployed across various social media platforms.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Frontiers in Big Data
Language English
Title The Effects of Gender Signals and Performance in Online Product Reviews
Volume 4
Number 77140
Year 2022
Reviewed? Y
URL https://www.frontiersin.org/articles/10.3389/fdata.2021.771404/full
DOI https://doi.org/10.3389/fdata.2021.771404
Open Access Y
Open Access Link https://doi.org/10.3389/fdata.2021.771404


Wachs, Johannes (Details)
Lemmerich, Florian (Uni Passau, Germany)
Sachdeva, Rachneet (RWTH Aachen University, Germany)
Sikdar, Sandipan (RWTH Aachen University, Germany)
Strohmaier, Markus (RWTH Aachen University, Germany)
Institute for Data, Process and Knowledge Management (AE Polleres) (Details)
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