Crowd Development

Abstract

Collaborative crowdsourcing is an increasingly common way of accomplishing work in our economy. Yet, we know very little about how the behavior of these crowds changes over time and how these dynamics impact their performance. In this paper, we take a group development approach that considers how the behavior of crowds change over time in anticipation and as a result of their evaluation and recognition. Towards this goal, this paper studies the collaborative behavior of groups comprised of editors of articles that have been recognized for their outstanding quality and given the Good Articles (GA) status and those that eventually become Featured Articles (FA) on Wikipedia. The results show that the collaborative behavior of GA groups radically changes just prior to their nomination. In particular, the GA groups experience increases in the level of activity, centralization of workload, and level of GA experience and decreases in conflict (i.e., reverts) among editors. After being promoted to GA, they converge back to their typical behavior and composition. This indicates that crowd behavior prior to their evaluation period is dramatically different than behavior before or after. In addition, the collaborative behaviors of crowds during their promotion to GA are predictive of whether they are eventually promoted to FA. Our findings shed new light on the importance of time in understanding the relationship between crowd performance and collaborative measures such as centralization, conflict and experience.

Quantifying the Impact of Biopics on Wikipedia Articles

“…where k denotes the number of days Hence, w i represents the list of revisions from day r i -k to r i + k. Our analysis approach is motivated by the work of Zhang et al (Zhang, Livneh, Budak, Robert Jr, & Romero, 2017)[28] in which they studied the impact of "nomination" on Wikipedia articles. For each movie m i , we find its contribution to Wikipedia article a i during the window length w i .…”

“…Past research has investigated external shocks' effects on crowd collaborations in other crowdsourcing platforms such as Wikipedia. For instance, Zhang et al (Zhang, Livneh, Budak, Robert Jr, & Romero, 2017) have shown the impact of category nomination on Wikipedia quality. They observed an increase in an overall contribution during the Good Articles nomination period.…”

Network traits driving knowledge evolution in open collaboration systems

“…There is ample evidence in literature showing correlational connections between network configurations and system-level knowledge outcomes [8,42,67,68], but there are few that use longitudinal design to observe the dynamic impact of network configurations on knowledge evolution [69]. Adopting a new approach for quantifying text-based content exploration, this study suggests that network configurations are significant predictors of knowledge artifacts' subsequent development trajectory [51].…”