The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics

Activity of modern scholarship creates online footprints galore. Along with traditional metrics of research quality, such as citation counts, online images of researchers and institutions increasingly matter in evaluating academic impact, decisions about grant allocation, and promotion. We examined 400 biographical Wikipedia articles on academics from four scientific fields to test if being featured in the world's largest online encyclopedia is correlated with higher academic notability (assessed through citation counts). We found no statistically significant correlation between Wikipedia articles metrics (length, number of edits, number of incoming links from other articles, etc.) and academic notability of the mentioned researchers. We also did not find any evidence that the scientists with better WP representation are necessarily more prominent in their fields. In addition, we inspected the Wikipedia coverage of notable scientists sampled from Thomson Reuters list of "highly cited researchers". In each of the examined fields, Wikipedia failed in covering notable scholars properly. Both findings imply that Wikipedia might be producing an inaccurate image of academics on the front end of science. By shedding light on how public perception of academic progress is formed, this study alerts that a subjective element might have been introduced into the hitherto structured system of academic evaluation.Comment: To appear in EPJ Data Science. To have the Additional Files and Datasets e-mail the corresponding autho

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

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

Mapping bilateral information interests using the activity of Wikipedia editors

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

SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs

We describe SynGraphy, a method for visually summarising the structure of large network datasets that works by drawing smaller graphs generated to have similar structural properties to the input graphs. Visualising complex networks is crucial to understand and make sense of networked data and the relationships it represents. Due to the large size of many networks, visualisation is extremely difficult; the simple method of drawing large networks like those of Facebook or Twitter leads to graphics that convey little or no information. While modern graph layout algorithms can scale computationally to large networks, their output tends to a common "hairball" look, which makes it difficult to even distinguish different graphs from each other. Graph sampling and graph coarsening techniques partially address these limitations but they are only able to preserve a subset of the properties of the original graphs. In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph. To verify the utility of this approach as compared to other graph visualisation algorithms, we perform an experimental evaluation in which we repeatedly asked experimental subjects (professionals in graph mining and related areas) to determine which of two given graphs has a given structural property and then assess which visualisation algorithm helped in identifying the correct answer. Our summarisation approach SynGraphy compares favourably to other techniques on a variety of networks.Comment: 24 page

(Don't) mention the war: A comparison of Wikipedia and Britannica articles on national histories

International audienc

E-commerce in Russia: Challenges and Opportunities : Russian e-commerce market for local and foreign enterpreneurs

The work presented in this thesis explores Russian e-commerce as a rapidly growing and challenging industry quickly gaining popularity. The main focus will be on how e-retail business is progressing in Russia and which opportunities it offers as well as what kind of challenges it is bringing to local and foreign entrepreneurs willing to start e-commerce business in the country

Analysing timelines of national histories across Wikipedia editions: A comparative computational approach

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

Analyse rigoureuse de surfaces selectives en frequence

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 81666 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc