A variety of fan-based wikis about episodic fiction (e.g., television shows, novels, movies) exist on the World Wide Web. These wikis provide a wealth of information about complex stories, but if readers are behind in their viewing they run the risk of encountering "spoilers" -- information that gives away key plot points before the intended time of the show's writers. Enterprising readers might browse the wiki in a web archive so as to view the page prior to a specific episode date and thereby avoid spoilers. Unfortunately, due to how web archives choose the "best" page, it is still possible to see spoilers (especially in sparse archives). In this paper we discuss how to use Memento to avoid spoilers. Memento uses TimeGates to determine which best archived page to give back to the user, currently using a minimum distance heuristic. We quantify how this heuristic is inadequate for avoiding spoilers, analyzing data collected from fan wikis and the Internet Archive. We create an algorithm for calculating the probability of encountering a spoiler in a given wiki article. We conduct an experiment with 16 wiki sites for popular television shows. We find that 38 of those pages are unavailable in the Internet Archive. We find that when accessing fan wiki pages in the Internet Archive there is as much as a 66 chance of encountering a spoiler. Using sample access logs from the Internet Archive, we find that 19 of actual requests to the Wayback Machine for wikia.com pages ended in spoilers. We suggest the use of a different minimum distance heuristic, minpast, for wikis, using the desired datetime as an upper bound.
Almedia, Mozafari, and Cho produced one of the first studies of the behavior of contributors to Wikipedia @cite_23 . The authors discover that there are distinct groups of Wikipedia contributors. They suggest that as the number of articles increases, the contributors' attention is split among more and more content, resulting in the larger number of revising contributors rather than article creators. This informs our notion of number of edits as a surrogate to the popularity of a page.
@cite_23
2065100127 2036291768 2110618133 2118157326
Wikipedia's success is often attributed to the large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the coordination needed to write an article collaboratively, adding contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors structure the work by doing the majority of it. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the "wisdom of the crowd" in online production environments. Open collaboration systems, such as Wikipedia, need to maintain a pool of volunteer contributors to remain relevant. Wikipedia was created through a tremendous number of contributions by millions of contributors. However, recent research has shown that the number of active contributors in Wikipedia has been declining steadily for years and suggests that a sharp decline in the retention of newcomers is the cause. This article presents data that show how several changes the Wikipedia community made to manage quality and consistency in the face of a massive growth in participation have ironically crippled the very growth they were designed to manage. Specifically, the restrictiveness of the encyclopedia’s primary quality control mechanism and the algorithmic tools used to reject contributions are implicated as key causes of decreased newcomer retention. Furthermore, the community’s formal mechanisms for norm articulation are shown to have calcified against changes—especially changes proposed by newer editors. This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles. Wikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. We present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, our model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. We examine the performance of the model across a variety of Wikipedia articles.