Gender, Topic, and Audience Response: An Analysis of User-Generated Content on Facebook

ACM Conference on Human Factors in Computing Systems (CHI)

Abstract

Although users generate a large volume of text on Facebook every day, we know little about the topics they choose to talk about, and how their network responds. Using Latent Dirichlet Allocation (LDA), we identify topics from more than half a million Facebook status updates and determine which topics are more likely to receive audience feedback, such as likes and comments.

Furthermore, as previous research suggests that men and women use language for different purposes, we examine gender differences in topics, finding that women tend to share more personal issues (e.g., family matters) and men discuss more general public events (e.g., politics and sports). Post topic predicts how many people will respond to it, and gender moderates the relationship between topic and audience responsiveness.

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