Gender, topic, and audience response: an analysis of user-generated content on facebook

Authors:
Yi-Chia Wang;Moira Burke;Robert E. Kraut
Affiliations:
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA;Facebook, Inc., Palo Alto, California, USA;Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2013

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Abstract

Although both men and women communicate frequently on Facebook, we know little about what they talk about, whether their topics differ 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 feedback, such as likes and comments. Women tend to share more personal topics (e.g., family matters), while men discuss more public ones (e.g., politics and sports). Generally, women receive more feedback than men, but "male" topics (those more often posted by men) receive more feedback, especially when posted by women.