Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other knowledge bases are based on extracting entity and event based knowledge from it. Online news, on the other hand, is an authoritative and rich source for emerging entities, events and facts relating to existing entities. In this work, we study the creation of entities in Wikipedia with respect to news by studying how entity and event based information flows from news to Wikipedia. We analyze the lag of Wikipedia (based on the revision history of the English Wikipedia) with 20 years of The New York Times dataset (NYT). We model and analyze the lag of entities and events, namely their first appearance in Wikipedia and in NYT, respectively. In our extensive experimental analysis, we find that almost 20 of the external references in entity pages are news articles encoding the importance of news to Wikipedia. Second, we observe that the entity-based lag follows a normal distribution with a high standard deviation, whereas the lag for news-based events is typically very low. Finally, we find that events are responsible for creation of emergent entities with as many as 12 of the entities mentioned in the event page are created after the creation of the event page.
goes into similar directions with our analysis. @cite_18 analyses the collaborator structure of Wikipedia. They further classify the collaborators into five different classes based on the number of revisions. Furthermore, they measure the population growth of the collaborators falling into the five different classes. In their paper the authors conclude an interesting observation of the shift of how content is mostly provided by collaborators with lower number of edits, due to the increased fraction of such users in the Wikipedia community structure. This, however, does not correlate with any decline of the content provided by collaborators with high number of edits, hence, is accounted to the higher fraction of low edit users. In contrast to the work from , we have a different focus in our analysis, namely that of entity and event lag in Wikipedia, without any distinction of the Wikipedia community structure. In @cite_12 the authors analyze several aspects of Wikipedia's editors. They conclude that the number of edits is decreasing. Another slightly related work @cite_3 analyzes the number of research papers about Wikipedia, here too they conclude that the number has been decreasing, however, papers that use Wikpedia's data has seen an increase.
@cite_18 @cite_3 @cite_12
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In this paper, we present a study of the collaborative writing process in Wikipedia. Our work is based on a corpus of 1,995 edits obtained from 891 article revisions in the English Wikipedia. We propose a 21-category classification scheme for edits based on Faigley and Witte’s (1981) model. Example edit categories include spelling error corrections and vandalism. In a manual multi-label annotation study with 3 annotators, we obtain an inter-annotator agreement of = 0.67. We further analyze the distribution of edit categories for distinct stages in the revision history of 10 featured and 10 non-featured articles. Our results show that the information content in featured articles tends to become more stable after their promotion. On the opposite, this is not true for non-featured articles. We make the resulting corpus and the annotation guidelines freely available. 1 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. Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p* ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia. 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.