The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Quantifying the invisible audience in social networks
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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Cites
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“So, some underestimation may also be due to reliance on the availability heuristic, which suggests that prominent examples will impact peoples’ estimates [35].”
BackgroundI tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience
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Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook
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Everyone's an influencer: quantifying influence on twitter
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Cited by
Tufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780
“Participant” Perceptions of Twitter Research Ethics
Fiesler, C., & Proferes, N. (2018) · Social Media + Society · doi:10.1177/2056305118763366 · Cited by 528
The Imagined Audience on Social Network Sites
Litt, E., & Hargittai, E. (2016) · Social Media + Society · doi:10.1177/2056305116633482 · Cited by 395
First I "like" it, then I hide it: Folk Theories of Social Feeds
Eslami, M., Karahalios, K., Sandvig, C., Vaccaro, K., Rickman, A., Hamilton, K., & Kirlik, A. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858494 · Cited by 375
Using APIs for Data Collection on Social Media
Lomborg, S., & Bechmann, A. (2014) · The Information Society · doi:10.1080/01972243.2014.915276 · Cited by 258
Metadata
- Authors
- Bernstein, M. S., Bakshy, E., Burke, M., & Karrer, B.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2013-04-27
- Pages
- 21-30
- OpenAlex
- W2117744325
- Cited by
- 355
- References
- 38
- FWCI
- 33.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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