Societal Controversies in Wikipedia Articles

Collaborative content creation inevitably reaches situations where different points of view lead to conflict. We focus on Wikipedia, the free encyclopedia anyone may edit, where disputes about content in controversial articles often reflect larger societal debates. While Wikipedia has a public edit history and discussion section for every article, the substance of these sections is difficult to phantom for Wikipedia users interested in the development of an article and in locating which topics were most controversial. In this paper we present Contropedia, a tool that augments Wikipedia articles and gives insight into the development of controversial topics. Contropedia uses an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when

Building Collaborative Capacities in Learners: The M/cyclopedia Project Revisited

In this paper we trace the evolution of a project using a wiki-based learning environment in a tertiary education setting. The project has the pedagogical goal of building learners’ capacities to work effectively in the networked, collaborative, creative environments of the knowledge economy. The paper explores the four key characteristics of a ‘produsage’ environment and identifies four strategic capacities that need to be developed in learners to be effective ‘produsers’ (user-producers). A case study is presented of our experiences with the subject New Media Technologies, run at Queensland University of Technology, Brisbane, Australia. This progress report updates our observations made at the 2005 WikiSym conference

Can Who-Edits-What Predict Edit Survival?

As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system or consist of a highly specialized predictor that is tailored to a specific peer-production system. In this work, we explore a different point in the solution space that goes beyond user reputation but does not involve any content-based feature of the edits. We view each edit as a game between the editor and the component of the project. We posit that the probability that an edit is accepted is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. We apply our model on Wikipedia and the Linux kernel, two examples of large-scale peer-production systems, and we seek to understand whether it can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. It is simple to implement, computationally inexpensive, and in addition it enables us to discover interesting structure in the data.Comment: Accepted at KDD 201