Skip to content

Abstract Search

Primary Submission Category: Matching

Assessing Gender Disparities in Textual Response on

Authors: Zhiyu Guo, Zach Branson, Reagan Mozer,

Presenting Author: Zhiyu Guo*

The age of social media has fostered many different online communities with distinct cultures, some with more serious gender disparity issues than others. For example, men and women may receive different reactions to posts they make in online forums, due entirely to their gender. But one can argue that the style and topics that men and women talk about on the forum might differ, thereby making it unclear if upvote differences are due to gender or other confounding factors between male and female posters. In this work, we assess whether posting as a male or female causes a change in upvotes to posts made on the /r/relationships subreddit of, a popular forum website. We look at all posts with self-label F or M in 2015. We combine topic modeling, sentiment analysis, and other state-of-the-art text quantification methods with both propensity score-based and cardinality matching methods to address these possible confounding effects. We compare several estimators for the average difference in upvotes between men and women: outcome regression estimators, inverse propensity score weighted estimators, and nonparametric doubly robust estimators both before and after matching. We find that matching followed by doubly robust estimation allows for a flexible analysis that also makes a fundamental causal inference assumption – positivity – more tenable. We discover that on r/relationships, women tend to get a slightly higher number of upvotes than men.