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

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

Controversy Analysis and Detection

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the filter bubble effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple list of 10 links . This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two themes: computational models of controversy, and controversies occurring in neighborhoods of topics. Our broad contributions are: (1) Presenting a theoretical framework for modeling controversy as contention among populations; (2) Constructing the first automated approach to detecting controversy on the web, using a KNN classifier that maps from the web to similar Wikipedia articles; and (3) Proposing a novel controversy detection in Wikipedia by employing a stacked model using a combination of link structure and similarity. We conclude this work by discussing the challenging technical, societal and ethical implications of this emerging research area and proposing avenues for future work

Understanding the effects of topic factors and threat exposure on motivation to participate in knowledge artefacts: The case of Wikipedia

Wikipedia’s unique feature that prompts voluntary knowledge creation makes it relevant for researchers to examine what motivates editors to contribute to the platform when there are no obvious compensations that they could receive in exchange of their efforts. Earlier studies have identified various encouraging factors of Wikipedia participation (e.g., fun, ideology, community aspect). In this dissertation, I undertook a psychology perspective and examined the issue with a focus on person-object-environment paradigm that has not been previously studied within the context of Wikipedia motivation. This paradigm explains the human behavior as a product of a person’s interest-oriented relationship with an object and with her/his environment. The aim of this dissertation was then to investigate motivation to work with Wikipedia (in terms of willingness to contribute to the articles and production of article measures) in relation to topic factors (object) and threat exposure (environment). Two laboratory and one Wikipedia textual analysis studies suggested that general (i.e., topic familiarity and controversiality) and specific characteristics (i.e., sentiment and psychological content) of a topic played significant roles in Wikipedia motivation. Specifically, working with familiar and controversial topics that had sociopolitical references increased engagement to Wikipedia articles. Results also suggested that Wikipedia community produced article measures (e.g., longer articles) related to content with both positive and negative sentiments. A closer examination on psychological content showed that affective (positive and negative emotion) and drive states (achievement, reward, power, affiliation and risk) were the best predictors of article production. With regards to threat exposure, although threat manipulations induced in the forms of mortality salience and uncertainty salience led to negative mood states, they did not result in any changes in people’s willingness to work with the articles. Overall, the findings suggest that Wikipedia motivation was significantly influenced by general familiar and controversial characteristics of the presented topic as well as positive/negative polarity and specific psychological orientations of the content. Threat-evoking environmental cues during Wikipedia use, on the other hand, did not seem to affect the motivation levels. These results support the human-oriented aspect of Wikipedia platform that is distinctively fostered by editors’ psychological, social and emotional interests

WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks

Wikipedia articles contain multiple links connecting a subject to other pages of the encyclopedia. In Wikipedia parlance, these links are called internal links or wikilinks. We present a complete dataset of the network of internal Wikipedia links for the $9$ largest language editions. The dataset contains yearly snapshots of the network and spans $17$ years, from the creation of Wikipedia in 2001 to March 1st, 2018. While previous work has mostly focused on the complete hyperlink graph which includes also links automatically generated by templates, we parsed each revision of each article to track links appearing in the main text. In this way we obtained a cleaner network, discarding more than half of the links and representing all and only the links intentionally added by editors. We describe in detail how the Wikipedia dumps have been processed and the challenges we have encountered, including the need to handle special pages such as redirects, i.e., alternative article titles. We present descriptive statistics of several snapshots of this network. Finally, we propose several research opportunities that can be explored using this new dataset.Comment: 10 pages, 3 figures, 7 tables, LaTeX. Final camera-ready version accepted at the 13TH International AAAI Conference on Web and Social Media (ICWSM 2019) - Munich (Germany), 11-14 June 201

Detecting Controversies in Online News Media

This paper sets out to detect controversial news reports using online discussions as a source of information. We define controversy as a public discussion that divides society and demonstrate that a content and stylometric analysis of these debates yields useful signals for extracting disputed news items. Moreover, we argue that a debate-based approach could produce more generic models, since the discussion architectures we exploit to measure controversy occur on many different platforms

Wikipedia as an arena and source for the public: a scandinavian comparison of "Islam"

Under embargo until: 3.10.2020This article compares Wikipedia as an arena and source for the public through analysis of articles on “Islam” across the three Scandinavian languages. Findings show that the Swedish article is continuously revised and adjusted by a fairly high number of contributors, with comparatively low concentration to a small group of top users. The Norwegian article is static, more basic, but still serves as a matter-of-factly presentation of Islam as religion to a stable amount of views. In contrast, the Danish article is at once more dynamic through more changes up until recently, it portrays Islam differently with a distinct focus on identity issues, and it is read less often. The analysis illustrates how studying Wikipedia can bring light to the receiving end of what goes on in the public sphere. The analysis also illustrates how our understanding of the online realm profits from “groundedness,” and how the comparison of similar sites in different languages can yield insights into cultural as well as political differences, and their implications.acceptedVersio