Publication

Quantifying the Invisible Audience in Social Networks

ACM Conference on Human Factors in Computing Systems (CHI)


Abstract

When you share content in an online social network, who is listening? Users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate. However, understanding this invisible audience can impact both science and design, since perceived audiences influence content production and self-presentation online.

In this paper, we combine survey and large-scale log data to examine how well users’ perceptions of their audience match their actual audience on Facebook. We find that social media users consistently underestimate their audience size for their posts, guessing that their audience is just 27% of its true size.

Qualitative coding of survey responses reveals folk theories that attempt to reverse-engineer audience size using feedback and friend count, though none of these approaches are particularly accurate. We analyze audience logs for 222,000 Facebook users’ posts over the course of one month and find that publicly visible signals – friend count, likes, and comments – vary widely and do not strongly indicate the audience of a single post.

Despite the variation, users typically reach 61% of their friends each month. Together, our results begin to reveal the invisible undercurrents of audience attention and behavior in online social networks.

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