Research:CSCW 2016
This page lists all Wikimedia-related research events and presentations taking place at the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing - CSCW 2019 (February 27 - March 2, 2016) in San Francisco, California. The complete program is also available.
Papers[edit]
Analyzing Organizational Routines in Online Knowledge Collaborations: A Case for Sequence Analysis in CSCW[edit]
B.C. Keegan, S. Lev, O. Arazy
(preprint)
Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration. We outline a research agenda for researchers in computer-supported cooperative work (CSCW) to understand the relationships, patterns, antecedents, and consequences of sequential behavior using methods already developed in fields like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns. We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration..
A Contingency View of Transferring and Adapting Best Practices within Online Communities[edit]
H. Zhu, R. E. Kraut, A. Kittur
(preprint)
Online communities, much like companies in the business world, often need to transfer “best practices” internally from one unit to another to improve their performance. Organizational scholars disagree about how much a recipient unit should modify a best practice when incorporating it. Some evidence indicates that modifying a practice that has been successful in one environment will introduce problems, undercut its effectiveness and harm the performance of the recipient unit. Other evidence, though, suggests that recipients need to adapt the practice to fit their local environment. The current research introduces a contingency perspective on practice transfer, holding that the value of modifications depends on when they are introduced and who introduces them. Empirical research on the transfer of a quality-improvement practice between projects within Wikipedia shows that modifications are more helpful if they are introduced after the receiving project has had experience with the imported practice. Furthermore, modifications are more effective if they are introduced by members who have experience in a variety of other projects.
Parting Crowds: Characterizing Divergent Interpretations in Crowdsourced Annotation Tasks[edit]
S. Kairam, J. Heer
(preprint)
Crowdsourcing is a common strategy for collecting the “gold standard” labels required for many natural language applications. Crowdworkers differ in their responses for many reasons, but existing approaches often treat disagreements as "noise" to be removed through filtering or aggregation. In this paper, we introduce the workflow design pattern of crowd parting: separating workers based on shared patterns in responses to a crowdsourcing task. We illustrate this idea using an automated clustering-based method to identify divergent, but valid, worker interpretations in crowdsourced entity annotations collected over two distinct corpora -- Wikipedia articles and Tweets. We demonstrate how the intermediate-level view provide by crowd-parting analysis provides insight into sources of disagreement not easily gleaned from viewing either individual annotation sets or aggregated results. We discuss several concrete applications for how this approach could be applied directly to improving the quality and efficiency of crowdsourced annotation tasks.
Posters[edit]
LeadWise: Using Online Bots to Recruite and Guide Expert Volunteers[edit]
C. Flores-Saviaga, S. Savage, D. Taraborelli
(preprint)
In order to help non-profits recruit volunteers with specialized knowledge we propose LeadWise, a system that uses social media bots to recruit and guide contributions from experts to assist non-profits in reaching their goals. We test the feasibility of using the system to recruit specialized volunteers for Wikipedia. We focus in particular on experts who can help Wikipedia in its objective of reducing the gender gap by covering more women in its articles. Our results show that LeadWise was able to obtain a noteworthy number of expert participants in a two week period with limited requests to targeted specialists.
Workshops[edit]
Breaking into new Data-Spaces: Infrastructure for Open Community Science[edit]
A. Halfaker, J. Morgan, Y. Pandian, E. Thiry, K. Schuster, A.J. Million, S. Goggins, D. Laniado
(workshop home)
Despite being freely accessible, open online community data can be difficult to use effectively. To access and analyze large amounts of data, researchers must become familiar with the meaning of data values. Then they must also find a way to obtain and process the datasets to extract their desired vectors of behavior and content. This process is fraught with problems that are solved over and over again by each research team/lab that breaks into a new dataset. Those who lack the necessary technical skills may never be able to start. In this workshop, we will experiment with technologies and documentation protocols designed to make the process of “breaking into” a new dataset easier. Participants will get to use these technologies to explore new datasets and we'll use their feedback to improve our systems and make recommendations to the field.
Attendees[edit]
Wikipedia/Wikimedia researchers attending the conference