Studying cooperation and conflict between authors with
            <i>history flow</i>
            visualizations

Viégas, Fernanda B.; Wattenberg, Martin; Dave, Kushal

doi:10.1145/985692.985765

Cited by 612 publications

(462 citation statements)

References 7 publications

Supporting

Mentioning

449

Contrasting

Unclassified

Order By: Relevance

“…All three types of edits are relatively frequent (see Halfaker, Kittur, Kraut, andRiedl (2009), Vigas, Wattenberg, andDave (2004), and Piskorski and Gorbatai (2013) for details on reversions) on Wikipedia; therefore, defining them correctly is important. We relegate the description of how we identify bot-edits to the appendix, but outline briefly how we deal with reversions and vandalism.…”

Section: Data Selectionmentioning

confidence: 99%

Cumulative Growth in User-Generated Content Production: Evidence from Wikipedia

Aaltonen

Seiler

2016

Management Science

View full text Add to dashboard Cite

Open content production platforms typically allow users to gradually create content and react to previous contributions. Using detailed edit-level data across a large number of Wikipedia articles, we investigate how past edits shape current editing activity. We find that cumulative past contributions, embodied by the current article length, lead to significantly more editing activity, while controlling for a host of factors such as popularity of the topic and platform-level growth trends. The magnitude of the e↵ect is large; content growth over an eight-year period would have been 45% lower in its absence. Our findings suggest other open content production environments are likely to also benefit from similar cumulative growth e↵ects. In the presence of such e↵ects, managerial interventions that increase content are amplified because they trigger further contributions.JEL Classification: D24, L23, L86, M11, O31

show abstract

Section: Data Selectionmentioning

confidence: 99%

Cumulative Growth in User-Generated Content Production: Evidence from Wikipedia

Aaltonen

Seiler

2016

Management Science

View full text Add to dashboard Cite

show abstract

“…In history flow [42], Wikipedia author contributions to a single article were positioned vertically by the change along the article's text. This visualization could use either physical or per-revision time.…”

Section: Background and Related Workmentioning

confidence: 99%

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

Isaacs

Bremer

Jusufi

et al. 2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1: Logical timeline and clustered logical timeline views from Ravel, a tool for visualizing parallel execution traces. Events are represented by boxes, colored by their wall-clock delay. The use of logical time reveals communication patterns and leverages developers' understanding of their program's structure. We use the logical time structure to cluster on any metric, which allow us to represent large-scale traces using explorable clusters while still depicting messages with full timelines for a subset of processes.Abstract-With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies -potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.

show abstract

“…Its software platform, MediaWiki, allows anybody with Internet access to read and edit shared articles. The most important criteria in Wikipedia's search for new quality assessment methods are the immediacy typical for social media as well as accuracy, which can be challenging [25]. The German Wikipedia chapter has deployed one such process in the form of the MediaWiki extension FlaggedRevs 8 .…”

Section: Example Applicationmentioning

confidence: 99%

Privacy-Friendly Incentives and Their Application to Wikipedia

Camenisch¹,

Groß²,

Hladky³

et al. 2010

Policies and Research in Identity Management

View full text Add to dashboard Cite

Double-blind peer review is a powerful method to achieve high quality and thus trustworthiness of user-contributed content. Facilitating such reviews requires incentives as well as privacy protection for the reviewers. In this paper, we present the concept of privacy-friendly incentives and discuss the properties required from it. We then propose a concrete cryptographic realization based on ideas from anonymous e-cash and credential systems. Finally, we report on our software's integration into the MediaWiki software.

show abstract

Studying cooperation and conflict between authors with history flow visualizations

Cited by 612 publications

References 7 publications

Cumulative Growth in User-Generated Content Production: Evidence from Wikipedia

Cumulative Growth in User-Generated Content Production: Evidence from Wikipedia

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

Privacy-Friendly Incentives and Their Application to Wikipedia

Contact Info

Product

Resources

About