Wikipedians are born, not made: a study of power editors on Wikipedia

By Katherine A. Panciera, Aaron L Halfaker, Loreen Terveen. 2009

305 citations recorded by the source corpus.

Subfields: Communication, Computer Science Applications, Sociology and Political Science

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Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “For example, sociological studies often use students who join a school in the same year to understand the effect of interventions [22, 30], and condition on the year in which people were born to understand people’s behavior, such as variations in financial decision-making [47] or opinions on issues…”
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits (2020, background): “While some study selfselection processes and their importance (Panciera, Halfaker, and Terveen 2009), others highlight the importance of learning (Tan and Lee 2015).”
“I updated the ”: The evolution of references in the English Wikipedia and the implications for altmetrics (2020, background): “…general perspectives on the producer side of Wikipedia (e.g., on who edits Wikipedia), general editing patterns (Flöck, Erdogan, & Acosta, 2017), who becomes a power editor (Panciera, Halfaker, & Terveen, 2009), or how editors collaborate (Kittur, Suh et al., 2007; Muri ć , Abeliuk et al., 2019).”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, background): “Although crowd-sourcing seeks contributions from potentially anyone, in practice previous work has shown that only a tiny group of contributors (often referred to as ‘power users’) produces most of the content [22, 25, 32, 33, 35, 43].”
Lurking? cyclopaths?: a quantitative lifecycle analysis of user behavior in a geowiki (2010, background): “Figure 6 shows that user activity in 2008 began to decline at the beginning of November and then increase in mid-March.”
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022, background): “3 Lastly, a relatively small number of people are responsible for most of the content (Panciera et al., 2009; Matei and Britt, 2017).”
Early detection of potential experts in question answering communities (2011, background): “[6] show that initial contributions of experts are measurably different from contributions of ordinary users in communities like Wikipedia.”