The Effects of Gender Signals and Performance in Online Product Reviews

TL;DR

This study investigates how gender signals and performance in online reviews influence their success, revealing context-specific biases in different product categories and providing tools for analyzing gender effects in social media.

Contribution

It introduces a method to detect gender signals and measure gendered review performance, highlighting their varied impact on review success across categories.

Findings

Gender signaling affects review helpfulness differently across categories.

Reviews by women are less helpful in Electronics but more helpful in Beauty.

The paper provides tools for studying gender effects in social media.

Abstract

This work quantifies the effects of signaling and performing gender on the success of reviews written on the popular amazon shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed below products. Differences in how gender-signaling and gender-performing review authors are received can lead to important biases in what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names, distinguishing 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 or 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 review success using matching experiments. 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, our work provides a general chain of tools for studying gender-specific effects across various social media platforms.