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.
@cite_5: 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.
@cite_9: Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our B asic model is designed based on the mutual dependency between article quality and their author authority. The P eer R eview model introduces the review behavior into measuring article quality. Finally, our P rob R eview models extend P eer R eview with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.
@cite_7: One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.
@cite_8: Wikipedia editors are uniquely motivated to collaborate around current and breaking news events. However, the speed, urgency, and intensity with which these collaborations unfold also impose a substantial burden on editors' abilities to effectively coordinate tasks and process information. We analyze the patterns of activity on Wikipedia following the 2011 Tōhoku earthquake and tsunami to understand the dynamics of editor attention and participation, novel practices employed to collaborate on these articles, and the resulting coauthorship structures which emerge between editors and articles. Our findings have implications for supporting future coverage of breaking news articles, theorizing about motivations to participate in online community, and illuminating Wikipedia's potential role in storing cultural memories of catastrophe.
Closely related work is done by @cite_8 @cite_5 @cite_9 . Their work, similarly to ours, focuses on the dynamics of Wikipedia's coverage of real world entities. @cite_8 , the authors consider emerging events like the T = o hoku catastrophe http: en.wikipedia.org wiki 2011_T tries to detect links between entities withing a knowledge base. The work done by @cite_7 uses social network theory measures, such as Katz index to find links between entities. This is related to our work since we analyze the co-referencing of entities within Wikipedia, their collaborator structure and interlinking with events in the Wikipedia's event portal. Such attributes of entities are used to analyze their implications on the entity lag in Wikipedia against news corpora.