123 research outputs found

    Multilinguals and Wikipedia Editing

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    This article analyzes one month of edits to Wikipedia in order to examine the role of users editing multiple language editions (referred to as multilingual users). Such multilingual users may serve an important function in diffusing information across different language editions of the encyclopedia, and prior work has suggested this could reduce the level of self-focus bias in each edition. This study finds multilingual users are much more active than their single-edition (monolingual) counterparts. They are found in all language editions, but smaller-sized editions with fewer users have a higher percentage of multilingual users than larger-sized editions. About a quarter of multilingual users always edit the same articles in multiple languages, while just over 40% of multilingual users edit different articles in different languages. When non-English users do edit a second language edition, that edition is most frequently English. Nonetheless, several regional and linguistic cross-editing patterns are also present

    Cross-language Wikipedia Editing of Okinawa, Japan

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    This article analyzes users who edit Wikipedia articles about Okinawa, Japan, in English and Japanese. It finds these users are among the most active and dedicated users in their primary languages, where they make many large, high-quality edits. However, when these users edit in their non-primary languages, they tend to make edits of a different type that are overall smaller in size and more often restricted to the narrow set of articles that exist in both languages. Design changes to motivate wider contributions from users in their non-primary languages and to encourage multilingual users to transfer more information across language divides are presented.Comment: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2015. AC

    Mapping bilateral information interests using the activity of Wikipedia editors

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    We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of information interests, shared values, and understanding. Nevertheless, interests still vary between countries around the world. This raises important questions about what today's world map of in- formation interests actually looks like and what factors cause the barriers of information exchange between countries. To quantitatively construct a world map of information interests, we devise a scalable statistical model that identifies countries with similar information interests and measures the countries' bilateral similarities. From the similarities we connect countries in a global network and find that countries can be mapped into 18 clusters with similar information interests. Through regression we find that language and religion best explain the strength of the bilateral ties and formation of clusters. Our findings provide a quantitative basis for further studies to better understand the complex interplay between shared interests and conflict on a global scale. The methodology can also be extended to track changes over time and capture important trends in global information exchange.Comment: 11 pages, 3 figures in Palgrave Communications 1 (2015

    Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media

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    Social media enables the rapid spread of many kinds of information, from memes to social movements. However, little is known about how information crosses linguistic boundaries. We apply causal inference techniques on the European Twitter network to quantify multilingual users' structural role and communication influence in cross-lingual information exchange. Overall, multilinguals play an essential role; posting in multiple languages increases betweenness centrality by 13%, and having a multilingual network neighbor increases monolinguals' odds of sharing domains and hashtags from another language 16-fold and 4-fold, respectively. We further show that multilinguals have a greater impact on diffusing information less accessible to their monolingual compatriots, such as information from far-away countries and content about regional politics, nascent social movements, and job opportunities. By highlighting information exchange across borders, this work sheds light on a crucial component of how information and ideas spread around the world.Comment: ICWSM 2023 (please cite accordingly); see https://github.com/juliamendelsohn/bridging-nations for data, models, and cod

    Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach

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    Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, we aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on Wikipedia. In particular, we study articles related to the history of all UN member states and compare them in 30 language editions. We develop a computational approach that allows to identify focal points quantitatively, and find that Wikipedia narratives about national histories (i) are skewed towards more recent events (recency bias) and (ii) are distributed unevenly across the continents with significant focus on the history of European countries (Eurocentric bias). We also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. We hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere

    Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia

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    In this project, we focus on English Wikipedia, one of the main user-contributed content systems, and study the problem of predicting what users will become inactive and stop contributing to the encyclopedia. We propose a predictive model leveraging frequent patterns appearing in user’s editing behavior as features to predict active vs. inactive Wikipedia users. Our experiments show that our method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users. Moreover, we study differences in editing behavior of inactive vs. active users to explain why some users are leaving and provide some rules explaining our predictive model

    When humans and machines collaborate: Cross-lingual Label Editing in Wikidata

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    The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data

    Does Astronomy research become too dated for the public? Wikipedia citations to Astronomy and Astrophysics journal articles 1996-2014

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    Astronomy is a natural science attracting substantial public interest. On a human scale, most individual celestial objects are essentially unchanging but is the same true for interest in astronomy research? This article uses the popular online encyclopedia Wikipedia as a proxy for public interest in academic research and assesses the extent to which it cites astronomy and astrophysics articles published between 1996 and 2014. Automatic Bing searches in Webometric Analyst were used to count the number of citations to astronomy and astrophysics articles from Wikipedia. The results show that older papers from before 2008 are increasingly less likely to be cited. This is true overall and in most of the major language versions of Wikipedia, although it may reflect editors’ interests rather than the public’s interests. This is consistent with a moderate tendency towards obsolescence in public interest in research, although it is probably affected by the dates on which most Wikipedia content on the topic was created. Papers may become obsolete if they report evidence that are later superseded by improved data or if they propose a model that is later replaced

    Tracking Knowledge Propagation Across Wikipedia Languages

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    In this paper, we present a dataset of inter-language knowledge propagation in Wikipedia. Covering the entire 309 language editions and 33M articles, the dataset aims to track the full propagation history of Wikipedia concepts, and allow follow-up research on building predictive models of them. For this purpose, we align all the Wikipedia articles in a language-agnostic manner according to the concept they cover, which results in 13M propagation instances. To the best of our knowledge, this dataset is the first to explore the full inter-language propagation at a large scale. Together with the dataset, a holistic overview of the propagation and key insights about the underlying structural factors are provided to aid future research. For example, we find that although long cascades are unusual, the propagation tends to continue further once it reaches more than four language editions. We also find that the size of language editions is associated with the speed of propagation. We believe the dataset not only contributes to the prior literature on Wikipedia growth but also enables new use cases such as edit recommendation for addressing knowledge gaps, detection of disinformation, and cultural relationship analysis
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