Feed me: motivating newcomer contribution in social network sites

By Moira Burke, Cameron A. Marlow, Thomas M. Lento. International Conference on Human Factors in Computing Systems, 2009

495 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/76ef633b906ef65f4f81a211fd5e27dda5ac2a9a

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Citations in the corpus

Is it really about me?: message content in social awareness streams (2010, background): “While a number of different research efforts examined activities on Facebook and other services where SAS are available [1,2,7,8], most of these studies did not address the unique aspects of SAS.”
First I "like" it, then I hide it: Folk Theories of Social Feeds (2016, background): “Burke et al. discuss how feedback and distribution on feeds affected newcomers contributions; a newcomer who received a comment on a photo was more likely to contribute more photos [5].”
Faces engage us: photos with faces attract more likes and comments on Instagram (2014, background): “Sharing content can in turn ensure that users remain engaged and committed in the future visits [13, 47].”
Social Barriers Faced by Newcomers Placing Their First Contribution in Open Source Software Projects (2015, background): “Burke et al. [8] analyzed Facebook newcomers and found that when newcomers learn by example and receive proper feedback, one can more easily predict their future contributions.”
Characterizing user behavior in online social networks (2009, background): “[3] studied user motivations for contributing in social networking sites, based on server log data from Facebook.”
Quantifying the invisible audience in social networks (2013, background): “Sharing volume and self-disclosure on Facebook are also correlated with audience size [10, 39].”
Steering user behavior with badges (2013, background): “The question of user participation and contribution in online domains is a broad topic that a number of active lines of research have contributed to, including socialpsychological studies of user engagement [5, 6], incentives for effort and high-quality contribution in social media [12], mechanisms…”
Overcoming the "Ideology of Openness": Probing the Affordances of Social Media for Organizational Knowledge Sharing (2013, background): “This provides a counterpoint to the prevalent assumption that social media will and should foster open communication and knowledge sharing (e.g., Burke et al., 2009) and shows that organizational members are also motivated to conceal information about themselves or the organization and may have…”
Predicting length of membership in online community "everything2" using feedback (2012, methodology): “Most prior research on user activity in online communities is conducted within a very short time frame—ranging from three months [2] to 16 months [3].”
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.”
Norm evolution and violation on Facebook (2012, background): “Findings from Facebook server-level data suggest that norms of sharing content are learned from observing the habits of Friends on the site (i.e. implicit norms); in other words, users contribute more content if they see Friends contributing content (Burke et al., 2009).”
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits (2020, background): “Thus, research on retention has primarily focused on limiting user churn and increasing contributions among newcomers (Burke, Marlow, and Lento 2009).”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “Research on social media use has also found evidence of social learning [18]: users monitor and adapt their behavior to what other users are doing.”
A Review of Facebook Research in the Social Sciences (2012, unknown): “To test the extent to which social learning motivates content sharing by new members, researchers analyzed data from 140,292 Facebook profiles, using information from their first 3 months of Facebook membership (Burke et al., 2009).”
Content or Community? A Digital Business Strategy for Content Providers in the Social Age (2013, background): “Burke et al. (2009) quantitatively examined photo contributions on Facebook and found that direct feedback on content is one of the factors related to the volume of content that a user subsequently uploads.”
Estimating peer effects in networks with peer encouragement designs (2016, background): “In the absence of this peer encouragement design, scientists and decision makers could instead rely on observational data to study the effects of receiving feedback (10, 55).”