Quantifying the invisible audience in social networks

By Michael S. Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer. 2013

385 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls (2014, background): “Thus, selecting on the dependent variable (the occurrence of war or revolution) can help identify necessary conditions, but those may not be sufficient.”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “Researchers have examined how people may attempt to reverse engineer systems based on their understanding of algorithms [5], how altering the design of system features affects an individual’s understanding of algorithms [21,24], and how understanding algorithms affects user behavior [25].”
LibRA (2019, neutral): “Individuals tend to share credible information because they have a conceptualization of an “invisible audience” [10], and since LinkedIn is a public space, they do not want to appear as dishonest [60].”
User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest (2016, background): “Many users do not comment and fewer still create posts; these users represent a large silent majority, a phenomenon which is observed on many other types of social media (Benevenuto et al. 2009; Bernstein et al. 2013; Gong, Lim, and Zhu 2015).”
"My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security (2015, background): “[5] proposed that visualizing the size of one’s audience on social media would help users understand the exposure of one’s posts.”
Silent Listeners: The Evolution of Privacy and Disclosure on Facebook (2013, background): “In addition to misconceptions about the access third-party apps gain to profile data, the literature has also documented users’ errors in estimating how many other Facebook members can have access to their profile data (see [2] and, more recently, [32] and [6]).”
Self-Censorship on Facebook (2013, background): “Balanced with the observation that many users desire larger audiences than they perceive they have for the content they share (Bernstein et al. 2013), we expected H6:”
Online Media Forums as Separate Social Lives: A Qualitative Study of Disclosure Within and Beyond Reddit (2015, background): “For example, Bernstein and his colleagues found that people dramatically underestimate the size of their audience for posts on Facebook (Bernstein et al., 2013).”
Quantifying the Invisible Labor in Crowd Work (2021, methodology): “For this part, we use the initial survey that we gave workers, which was inspired by prior work [4].”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “Since activity feeds are a primary interface element in most social networks that expose people to their friends’ updates [47, 33, 10, 7] and facilitate social learning [12, 20], we look specifically at how influence might flow through these feeds.”
Generating brand awareness in Online Social Networks (2015, background): “Each user who posts on a popular OSN, such as Facebook, is seen by an average 35% of their friends or connections (Bernstein, Bakshy, Burke, & Karrer, 2013).”
Self‐Disclosure in Social Media: Extending the Functional Approach to Disclosure Motivations and Characteristics on Social Network Sites (2014, background): “…for audience construction are not yet well understood, several studies point out to the role of active or interaction audience in perceptions of imagined audience as people rely on others' posts and likes to determine who is listening to them (Bernstein et al., 2013; Kivran-Swaine & Naaman, 2011).”
"I always assumed that I wasn't really that close to [her]": Reasoning about Invisible Algorithms in News Feeds (2015, background): “The invisibility of audiences in online environments has prompted research into the imagined audience [23], including quantifying how perceived audiences compare to actual audiences and measuring otherwise invisible patterns of attention on social media [4].”
First I "like" it, then I hide it: Folk Theories of Social Feeds (2016, background): “Feed assumptions and outcomes: Bernstein et al. explored Facebook users’ imagined audience [3], discovering that people used information in the interface such as friend count, likes, and comments to develop folk theories to estimate the relationship between these indicators and the size of their…”
“Participant” Perceptions of Twitter Research Ethics (2018, background): “As with many social media platforms, Twitter users also often have an “imagined audience” that may not match to reality (Bernstein, Bakshy, Burke, & Karrer, 2013; Litt, 2012; Marwick & Boyd, 2011).”