Massive ontology interface

This paper describes the Massive Ontology Interface (MOI), a web portal which facilitates interaction with a large ontology (over 200,000 concepts and 1.6M assertions) that is built automatically using OpenCyc as a backbone. The aim of the interface is to simplify interaction with the massive amounts of information and guide the user towards understanding the ontology’s data. Using either a text or graph-based representation, users can discuss and edit the ontology. Social elements utilizing gamification techniques are included to encourage users to create and collaborate on stored knowledge as part of a web community. An evaluation by 30 users comparing MOI with OpenCyc’s original interface showed significant improvements in user understanding of the ontology, although full testing of the interface’s social elements lies in the future

Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia

Collaborative crowdsourcing has become a popular approach to organizing work across the globe. Being global also means being vulnerable to shocks -- unforeseen events that disrupt crowds -- that originate from any country. In this study, we examine changes in collaborative behavior of editors of Chinese Wikipedia that arise due to the 2005 government censor- ship in mainland China. Using the exogenous variation in the fraction of editors blocked across different articles due to the censorship, we examine the impact of reduction in group size, which we denote as the shock level, on three collaborative behavior measures: volume of activity, centralization, and conflict. We find that activity and conflict drop on articles that face a shock, whereas centralization increases. The impact of a shock on activity increases with shock level, whereas the impact on centralization and conflict is higher for moderate shock levels than for very small or very high shock levels. These findings provide support for threat rigidity theory -- originally introduced in the organizational theory literature -- in the context of large-scale collaborative crowds

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table

MoodBar: Increasing new user retention in Wikipedia through lightweight socialization

Socialization in online communities allows existing members to welcome and recruit newcomers, introduce them to community norms and practices, and sustain their early participation. However, socializing newcomers does not come for free: in large communities, socialization can result in a significant workload for mentors and is hard to scale. In this study we present results from an experiment that measured the effect of a lightweight socialization tool on the activity and retention of newly registered users attempting to edit for the first time Wikipedia. Wikipedia is struggling with the retention of newcomers and our results indicate that a mechanism to elicit lightweight feedback and to provide early mentoring to newcomers improves their chances of becoming long-term contributors.Comment: 9 pages, 5 figures, accepted for presentation at CSCW'1

Building automated vandalism detection tools for Wikidata

Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89% of vandalism while reducing patrollers' workload by 98%, by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit

Education Unleashed: Participatory Culture, Education, and Innovation in Second Life

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and LearningWhile virtual worlds share common technologies and audiences with games, they possess many unique characteristics. Particularly when compared to massively multiplayer online role-playing games, virtual worlds create very different learning and teaching opportunities through markets, creation, and connections to the real world, and lack of overt game goals. This chapter aims to expose a wide audience to the breadth and depth of learning occurring within Second Life (SL). From in-world classes in the scripting language to mixed-reality conferences about the future of broadcasting, a tremendous variety of both amateurs and experts are leveraging SL as a platform for education. In one sense, this isn't new since every technology is co-opted by communities for communication, but SL is different because every aspect of it was designed to encourage this co-opting, this remixing of the virtual and the real

Energy Counselling and Modern IT. Drawing on Web 2.0 for a Greener World

The aim of this article is to explore how modern IT solutions for collaborative knowledge evolution could lead to more effective energy counselling and increased energy knowledge among the public. Comparative studies have been performed where the focus has been on the prerequisites for effective use of web 2.0 type collaboration and wikis. The research is primarily aimed at actors within the energy sector, although similar developments also take place in other sectors. Targeted investments employing collaborative IT to involve the public in energy counselling could lead to lower energy consumption and an increased consciousness of environmental issues in the society. A conclusion is that web 2.0-like initiatives could play a valuable role in the knowledge development and exchange between energy counsellors, and further the knowledge exchange between the counsellors, the regional energy agencies and the public. They could also help channel an energy interest among the public into a collaborative knowledge production, and contribute to a good quality factual basis for the conceptions that develop in society. This would strengthen both the energy counselling and the energy counsellor corps.communities, sustainability, sector transcendence, energy counselling, web 2.0.

A User-Centered Concept Mining System for Query and Document Understanding at Tencent

Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy are core research problems in text understanding and support many downstream tasks such as query analysis, knowledge base construction, recommendation, and search. However, we argue that most prior studies extract formal and overly general concepts from Wikipedia or static web pages, which are not representing the user perspective. In this paper, we describe our experience of implementing and deploying ConcepT in Tencent QQ Browser. It discovers user-centered concepts at the right granularity conforming to user interests, by mining a large amount of user queries and interactive search click logs. The extracted concepts have the proper granularity, are consistent with user language styles and are dynamically updated. We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser. We performed extensive offline evaluation to demonstrate that our approach could extract concepts of higher quality compared to several other existing methods. Our system has been deployed in Tencent QQ Browser. Results from online A/B testing involving a large number of real users suggest that the Impression Efficiency of feeds users increased by 6.01% after incorporating the user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201