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快乐十分模拟投注器:Preventing harassment and increasing group participation through social norms in 2,190 online science discussions
广东快乐十分投注下载 www.hmclip.net Edited by Rosanna Guadagno, Stanford University, Stanford, CA, and accepted by Editorial Board Member Jennifer A. Richeson March 22, 2019 (received for review September 7, 2018)
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Online harassment remains a common experience despite decades of work to identify unruly behavior and enforce rules against it. Consequently, many people avoid participating in online conversations for fear of harassment. Using a large-scale field experiment in a community with 13 million subscribers, I show that it is possible to prevent unruly behavior and also increase newcomer participation in public discussions of science. Announcements of community rules in discussions increased the chance of rule compliance by >8 percentage points and increased newcomer participation by 70% on average. This study demonstrates the influence of community rules on who chooses to join a group and how they behave.
Theories of human behavior suggest that people’s decisions to join a group and their subsequent behavior are influenced by perceptions of what is socially normative. In online discussions, where unruly, harassing behavior is common, displaying community rules could reduce concerns about harassment that prevent people from joining while also influencing the behavior of those who do participate. An experiment tested these theories by randomizing announcements of community rules to large-scale online conversations in a science-discussion community with 13 million subscribers. Compared with discussions with no mention of community expectations, displaying the rules increased newcomer rule compliance by >8 percentage points and increased the participation rate of newcomers in discussions by 70% on average. Making community norms visible prevented unruly and harassing conversations by influencing how people behaved within the conversation and also by influencing who chose to join.
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Author contributions: J.N.M. designed research, performed research, analyzed data, and wrote the paper.
The author declares no conflict of interest.
This article is a PNAS Direct Submission. R.G. is a guest editor invited by the Editorial Board.
Data deposition: Code and data for this paper are available at https://github.com/mitmedialab/CivilServant-Analysis/tree/master/papers/r_science_2016.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1813486116/-/DCSupplemental.
Published under the PNAS license.