Wikipedia as an Academic Reference: Faculty and Student Viewpoints

Wikis are becoming popular with business and academia as a way to harvest, archive, and manage knowledge. One of the most popular and well-known wikis is Wikipedia, the online encyclopedia started by Jimmy Wales and Larry Sanger in 2001. Since its inception, much has been written (both pro and con) about Wikipedia; however, Wikipedia is one of the most popular sites on the Internet today. As its popularity increases, more and more “net generation” students will be utilizing its articles as reference sources for academic work. This paper explores the emerging “wiki way” of Web 2.0 tools and highlights the good, the bad, and the management of Wikipedia as an academic reference. Further, this paper benchmarks how faculty and students are using Wikipedia, as well as exploring their viewpoint on using this information in the academic environment

Use of wikis as a collaborative ICT tool for extending the frontiers of knowledge in tertiary institutions

The human brain works much like a network of computers connected by nodes. These nodes allow computers on the same network to communicate effectively. Educators have discovered that today’s learning environment functions much the same way, with learners connecting to the internet, to other learners and to their teachers to increase their knowledge. This discovery has led to a paradigm shift in education which has transformed the learning environment from teacher-centered to learner-centered. The learner-centered environment allows for interactivity, communication and collaboration. When Web 2.0 technologies are used in the classroom, learners and teachers are given the opportunity to extend the frontiers of knowledge by collaborating and contributing to knowledge. This paper explores the possibility of using Wikis – a Web 2.0 technology – to extend the frontiers of knowledge. It also discusses how Wikis are presently being used in education; how to create a Wiki site using three different Wiki host platforms; and how to contribute content to Wikipedia – which is the world’s largest Wiki site. Finally, recommendations are given on what management of institutions can do to encourage the use of Wikis in the classroom.KEYWORDS: Collaboration, Web 2.0 technology, Wikis, Wikipedia, 21st century skills, Frontiers of knowledg

Assessing Knowledge Organization Systems from a gender perspective: Wikipedia Taxonomy and Wikidata Ontologies

Develop a comprehensive framework for assessing the knowledge organization system (KOS), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender non-binary perspective.This study employs heuristic and inspection methods to assess Wikipedia's Knowledge Organization Systems, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia's taxonomy for advantages and enhancements.This study evaluates Wikipedia's taxonomy and Wikidata's ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia's categorization challenges.The assessment of Wikipedia categories (taxonomy) based on Knowledge Organization System standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall knowledge organization system to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.</p

Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too

Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations

This paper studies the conversations behind the operations of a large-scale, online knowledge production community: Wikipedia. We investigate gender differences in the conversational styles (emotionality) and conversational domain choices (controversiality and gender stereotypicality of content) among contributors, and how these differences change as we look up the organizational hierarchy. In the general population of contributors, we expect and find significant gender differences, whereby comments and statements from women are higher-valenced, have more affective content, and are in domains that are less controversial and more female-typed. Importantly, these differences diminish or disappear among people in positions of power: female authorities converge to the behavior of their male counterparts, such that the gender gaps in valence and willingness to converse on controversial content disappear. We find greater sorting into topics according to their gender stereotypicality. We discuss mechanisms and implications for research on gender differences, leadership behavior, and conversational phenomena arising from such large-scale forms of knowledge production

Exploring the Relationship between Membership Turnover and Productivity in Online Communities

One of the more disruptive reforms associated with the modern Internet is the emergence of online communities working together on knowledge artefacts such as Wikipedia and OpenStreetMap. Recently it has become clear that these initiatives are vulnerable because of problems with membership turnover. This study presents a longitudinal analysis of 891 WikiProjects where we model the impact of member turnover and social capital losses on project productivity. By examining social capital losses we attempt to provide a more nuanced analysis of member turnover. In this context social capital is modelled from a social network perspective where the loss of more central members has more impact. We find that only a small proportion of WikiProjects are in a relatively healthy state with low levels of membership turnover and social capital losses. The results show that the relationship between social capital losses and project performance is U-shaped, and that member withdrawal has significant negative effect on project outcomes. The results also support the mediation of turnover rate and network density on the curvilinear relationship

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods