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Cosley, Dan; Frankowski, Dan; Terveen, Loren; Riedl, John

doi:10.1145/1216295.1216309

Cited by 186 publications

(25 citation statements)

References 39 publications

(33 reference statements)

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“…Likewise, readership patterns, category structures, and templates could be used to design a more active content suggestion system for Wikipedia readers. Previous work indicates that surfacing past activities or interests for editors can mobilize them to make more contributions [4]. Our findings here imply that similar interventions could help readers navigate a topically-linked set of articles related to unfolding events or movements.…”

Section: Implications For Practice and Designsupporting

confidence: 56%

Black Lives Matter in Wikipedia

Twyman

Keegan

Shaw

2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise preexisting knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

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Section: Implications For Practice and Designsupporting

confidence: 56%

Black Lives Matter in Wikipedia

Twyman

Keegan

Shaw

2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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“…The entire edit history of Wikipedia is freely available for analysis, making it attractive for research (see e.g. [5,25,23,26]). Wikipedia has a rich social structure in which a large number of users interact during the course of producing articles.…”

Section: Analyzing Similarity Over Timementioning

confidence: 99%

“…We set the history length parameter k to 10 for both the learning and the simulations presented in the next section, although we find that the simulation results are insensitive to k over the range [5,100]. The result of the parameter estimation process was as follows.…”

Section: Activities and Interactionsmentioning

confidence: 99%

Feedback effects between similarity and social influence in online communities

Crandall

Cosley

Huttenlocher

et al. 2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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514

338

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A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings.We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know

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“…Knowledge-intensive tasks can not be solved by anyone in the crowd. There are different approaches to obtain high quality results, contributors can for example be matched with tasks (Cosley et al, 2007;Difallah et al, 2013). von Ahn and Dabbish (2004) proposed theme rooms, clustering tasks by domain and leaving the choice to the contributor.…”

Section: Related Workmentioning

confidence: 99%

Group Minds and the Case of Wikipedia

DeDeo

2014

HCj

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Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors, showing how some, but not all, effects of individual experience persist in the aggregate. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions, and attributions of cognitive states of the form "what the group believes" and "what the group values."

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Cited by 186 publications

References 39 publications

Black Lives Matter in Wikipedia

Black Lives Matter in Wikipedia

Feedback effects between similarity and social influence in online communities

Group Minds and the Case of Wikipedia

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