Successful online communities have complex cooperative arrangements, articulations of work, and integration practices. They require technical infrastructure to support a broad division of labor. Yet the research literature lacks empirical studies that detail which types of work are valued by participants in an online community. A content analysis of Wikipedia barnstars – personalized tokens of appreciation given to participants – reveals a wide range of valued work extending far beyond simple editing to include social support, administrative actions, and types of articulation work. Our analysis develops a theoretical lens for understanding how wiki software supports the creation of articulations of work. We give implications of our results for communities engaged in large-scale collaborations.

Blatantly unproductive edits undermine the quality of the collaboratively-edited encyclopedia, Wikipedia. They not only disseminate dishonest and offensive content, but force editors to waste time undoing such acts of vandalism. Language-processing has been applied to combat these malicious edits, but as with email spam, these filters are evadable and computationally complex. Meanwhile, recent research has shown spatial and temporal features effective in mitigating email spam, while being lightweight and robust. In this paper, we leverage the spatio-temporal properties of revision metadata to detect vandalism on Wikipedia. An administrative form of reversion called rollback enables the tagging of malicious edits, which are contrasted with nonoffending edits in numerous dimensions. Crucially, none of these features require inspection of the article or revision text. Ultimately, a classifier is produced which flags vandalism at performance comparable to the natural-language efforts we intend to complement (85 % accuracy at 50 % recall). The classifier is scalable (processing 100+ edits a second) and has been used to locate over 5,000 manually-confirmed incidents of vandalism outside our labeled set.

This paper proposes an active learning approach using language model statistics to detect Wikipedia vandalism. Wikipedia is a popular and influential collaborative information system. The collaborative nature of authoring, as well as the high visibility of its content, have exposed Wikipedia articles to vandalism. Vandalism is defined as malicious editing intended to compromise the integrity of the content of articles. Extensive manual efforts are being made to combat vandalism and an automated approach to alleviate the laborious process is needed. This paper builds statistical language models, constructing distributions of words from the revision history of Wikipedia articles. As vandalism often involves the use of unexpected words to draw attention, the fitness (or lack thereof) of a new edit when compared with language models built from previous versions may well indicate that an edit is a vandalism instance. In addition, the paper adopts an active learning model to solve the problem of noisy and incomplete labeling of Wikipedia vandalism. The Wikipedia domain with its revision histories offers a novel context in which to explore the potential of language models in characterizing author intention. As the experimental results presented in the paper demonstrate, these models hold promise for vandalism detection.

We consider the problem of measuring user contributions to versioned, collaborative bodies of information, such as wikis. Measuring the contributions of individual authors can be used to divide revenue, to recognize merit, to award status promotions, and to choose the order of authors when citing the content. In the context of the Wikipedia, previous works on author contribution estimation have focused on two criteria: the total text created, and the total number of edits performed. We show that neither of these criteria work well: both techniques are vulnerable to manipulation, and the totaltext criterion fails to reward people who polish or re-arrange the content. We consider and compare various alternative criteria that take into account the quality of a contribution, in addition to the quantity, and we analyze how the criteria differ in the way they rank authors according to their contributions. As an outcome of this study, we propose to adopt total edit longevity as a measure of author contribution. Edit longevity is resistant to simple attacks, since edits are counted towards an author’s contribution only if other authors accept the contribution. Edit longevity equally rewards people who create content, and people who rearrange or polish the content. Finally, edit longevity distinguishes the people who contribute little (who have contribution close to zero) from spammers or vandals, whose contribution quickly grows negative.

Prior research on Wikipedia has characterized the growth in content and editors as being fundamentally exponential in nature, extrapolating current trends into the future. We show that recent editing activity suggests that Wikipedia growth has slowed, and perhaps plateaued, indicating that it may have come against its limits to growth. We measure growth, population shifts, and patterns of editor and administrator activities, contrasting these against past results where possible. Both the rate of page growth and editor growth has declined. As growth has declined, there are indicators of increased coordination and overhead costs, exclusion of newcomers, and resistance to new edits. We discuss some possible explanations for these new developments in Wikipedia including decreased opportunities for sharing existing knowledge and increased bureaucratic stress on the socio-technical system itself.

WikiTrust is a reputation system for Wikipedia authors and content. WikiTrust computes three main quantities: edit quality, author reputation, and content reputation. The edit quality measures how well each edit, that is, each change introduced in a revision, is preserved in subsequent revisions. Authors who perform good quality edits gain reputation, and text which is revised by several high-reputation authors gains reputation. Since vandalism on the Wikipedia is usually performed by anonymous or new users (not least because long-time vandals end up banned), and is usually reverted in a reasonably short span of time, edit quality, author reputation, and content reputation are obvious candidates as features to identify vandalism on the Wikipedia. Indeed, using the full set of features computed by WikiTrust, we have been able to construct classifiers that identify vandalism with a recall of 83.5%, a precision of 48.5%, and a false positive rate of 8%, for an area under the ROC curve of 93.4%. If we limit ourselves to the set of features available at the time an edit is made (when the edit quality is still unknown), the classifier achieves a recall of 77.1%, a precision of 36.9%, and a false positive rate of 12.2%, for an area under the ROC curve of 90.4%. Using these classifiers, we have implemented a simple Web API that provides the vandalism estimate for every revision of the English Wikipedia. The API can be used both to identify vandalism that needs to be reverted, and to select highquality, non-vandalized recent revisions of any given Wikipedia article. These recent high-quality revisions can be included in static snapshots of the Wikipedia, or they can be used whenever tolerance to vandalism is low (as in a school setting, or whenever the material is widely disseminated).

In content-driven reputation systems for collaborative content, users gain or lose reputation according to how their contributions fare: authors of long-lived contributions gain reputation, while authors of reverted contributions lose reputation. Existing content-driven systems are prone to Sybil attacks, in which multiple identities, controlled by the same person, perform coordinated actions to increase their reputation. We show that content-driven reputation systems can be made resistent to such attacks by taking advantage of the fact that the reputation increments and decrements depend on content modifications, which are visible to all. We present an algorithm for content-driven reputation that prevents a set of identities from increasing their maximum reputation without doing any useful work. Here, work is considered useful if it causes content to evolve in a direction that is consistent with the actions of high-reputation users. We argue that the content modifications that require no effort, such as the insertion or deletion of arbitrary text, are invariably non-useful. We prove a truthfullness result for the resulting system, stating that users who wish to perform a contribution do not gain by employing complex contribution schemes, compared to simply performing the contribution at once. In particular, splitting the contribution in multiple portions, or employing the coordinated actions of multiple identities, do not yield additional reputation. Taken together, these results indicate that content-driven systems can be made robust with respect to Sybil attacks.

The success of Wikipedia as a large-scale collaborative effort has spurred researchers to examine the motivations and behaviors of Wikipedia’s participants. However, this research has tended to focus on active involvement rather than more common forms of participation such as reading. In this paper we argue that Wikipedia’s readers should not all be characterized as free-riders – individuals who knowingly choose to take advantage of others ’ effort. Furthermore, we illustrate how readers provide a valuable service to Wikipedia. Finally, we use the notion of legitimate peripheral participation to argue that reading is a gateway activity through which newcomers learn about Wikipedia. We find support for our arguments in the results of a survey of Wikipedia usage and knowledge. Implications for future research and design are discussed.

Socially mediating technologies (SMTs) like blogs, web forums, Slashdot and Wikipedia, have drastically changed the equation of mass social interaction online. As individuals participate and contribute content, many of their actions are visible to other participants. The work of making a contribution, supporting and maintaining the community are equally valuable to understanding

This paper explores a statistical language modeling approach for detecting Wikipedia vandalism. Wikipedia is a popular and influential collaborative information system. The collaborative nature of authoring, as well as the high visibility of its content, have exposed Wikipedia articles to vandalism, defined as malicious editing intended to compromise the integrity of the content of articles. Extensive manual efforts are being made to combat vandalism and an automated approach to alleviate the laborious process is essential. This paper offers first a categorization of Wikipedia vandalism types and identifies technical challenges associated with detecting each category. In addition, this paper builds statistical language models, constructing distributions of words from the revision history of Wikipedia articles. As vandalism often involves the use of unexpected words to draw attention, the fitness (or lack thereof) of a new edit when compared with language models built from previous versions may well indicate that an edit is a vandalism instance. The Wikipedia domain with its revision histories offers a novel context in which to explore the potential of language models in characterizing author intention. As the experimental results presented in the paper demonstrate, these models hold promise for vandalism detection.