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 Basic model is designed based on the mutual dependency between article quality and their author authority. The PeerReview model introduces the review behavior into measuring article quality. Finally, our ProbReview models extend PeerReview 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.

Wikipedia, the online encyclopedia, has attracted attention both because of its popularity and its unconventional policy of letting anyone on the internet edit its articles. This paper describes the results of an empirical analysis of Wikipedia and discusses ways in which the Wikipedia community has evolved as it has grown. We contrast our findings with an earlier study [11] and present three main results. First, the community maintains a strong resilience to malicious editing, despite tremendous growth and high traffic. Second, the fastest growing areas of Wikipedia are devoted to coordination and organization. Finally, we focus on a particular set of pages used to coordinate work, the “Talk” pages. By manually coding the content of a subset of these pages, we find that these pages serve many purposes, notably supporting strategic planning of edits and enforcement of standard guidelines and conventions. Our results suggest that despite the potential for anarchy, the Wikipedia community places a strong emphasis on group coordination, policy, and process.

Wikis are collaborative systems in which virtually anyone can edit anything. Although wikis have become highly popular in many domains, their mutable nature often leads them to be distrusted as a reliable source of information. Here we describe a social dynamic analysis tool called WikiDashboard which aims to improve social transparency and accountability on Wikipedia articles. Early reactions from users suggest that the increased transparency afforded by the tool can improve the interpretation, communication, and trustworthiness of Wikipedia articles.

As collaborative repositories grow in popularity and use, issues concerning the quality and trustworthiness of information grow. Some current popular repositories contain contributions from a wide variety of users, many of which will be unknown to a potential end user. Additionally the content may change rapidly and information that was previously contributed by a known user may be updated by an unknown user. End users are now faced with more challenges as they evaluate how much they may want to rely on information that was generated and updated in this manner. A trust management layer has become an important requirement for the continued growth and acceptance of collaboratively developed and maintained information resources. In this paper, we will describe our initial investigations into designing and implementing an extensible trust management layer for collaborative and/or aggregated repositories of information. We leverage our work on the Inference

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users. 1.

Abstract. Wikis are successful tools for collaborative information collection. Wikis are becoming popular knowledge management tools, but do not fully support the requirements for such tools, namely structured search and knowledge reuse. Adding semantic annotations to Wikis helps to address these limitations by offering advanced information access (navigation and querying) and allowing knowledge reuse (through embedded queries and semantic information exchange). We present an architecture for Semantic Wikis and present our prototype SemperWiki. 1

Abstract: Wikipedia represents an intriguing new publishing paradigm—can it be used to engage students in authentic collaborative writing activities? How can we design wiki publishing tools and curricula to support learning among student authors? We suggest that wiki publishing environments can create learning opportunities that address four dimensions of authenticity: personal, real world, disciplinary, and assessment. We have begun a series of design studies to investigate links between wiki publishing experiences and writing-to-learn. The results of an initial study in an undergraduate government course indicate that perceived audience plays an important role in helping students monitor the quality of writing; however, students ’ perception of audience on the Internet is not straightforward. This preliminary iteration resulted in several guidelines that are shaping efforts to design and implement new wiki publishing tools and curricula for students and teachers. Wikipedia: This Just Doesn’t Make Sense A perplexing phenomenon has emerged online. Thousands of individuals have come together in one online community with the goal of building an encyclopedia of all human knowledge. This community relies on the work of volunteers, does not solicit contributions from experts, employs no formal review process, and allows people to

We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English WordNet glosses, and GermaNet pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.

Wikipedia has become one of the most important information resources on the Web by promoting peer collaboration and enabling virtually anyone to edit anything. However, this mutability also leads many to distrust it as a reliable source of information. Although there have been many attempts at developing metrics to help users judge the trustworthiness of content, it is unknown how much impact such measures can have on a system that is perceived as inherently unstable. Here we examine whether a visualization that exposes hidden article information can impact readers ’ perceptions of trustworthiness in a wiki environment. Our results suggest that surfacing information relevant to the stability of the article and the patterns of editor behavior can have a significant impact on users ’ trust across a variety of page types.

Wikipedia 1 is a very large and successful Web 2.0 example. As the number of Wikipedia articles and contributors grows at a very fast pace, there are also increasing disputes occurring among the contributors. Disputes often happen in articles with controversial content. They also occur frequently among contributors who are “aggressive ” or controversial in their personalities. In this paper, we aim to identify controversial articles in Wikipedia. We propose three models, namely the Basic model and two Controversy Rank (CR) models. These models draw clues from collaboration and edit history instead of interpreting the actual articles or edited content. While the Basic model only considers the amount of disputes within an article, the two Controversy Rank models extend the former by considering the relationships between articles and contributors. We also derived enhanced versions of these models by considering the age of articles. Our experiments on a collection of 19,456 Wikipedia articles shows that the Controversy Rank models can more effectively determine controversial articles compared to the Basic and other baseline models.