Mining cross-cultural relations from Wikipedia

Abstract

This qualitative and interpretative work aims at approaching Wikipedia's cultural facet from a transdisciplinary view. For this purpose, we set to analyse the existing literature that has dealt with Wikipedia in the generic media and in academic publications on the fields of Artificial Intelligence, Information and Education Sciences and Translation Studies as well. In these works-mostly with a descriptive, empirical or pragmatic approach-Wikipedia is referred to as an online collaborative encyclopaedia that incorporates innovation and content creation devices from the free software movement. Our proposal calls for resorting to theoretical works envisaging Wikipedia's cultural dimension. With a semiotic approach, we have based our analysis hypothesis on the social imaginary proposed by Vázquez Medel, as well as on the Polysystem Theory written by Even-Zohar. We will outline that these authors provide many clues for approaching Wikipedia as a cultural system boasting as a repository of knowledge and human relationships.

Linguistic neighbourhoods: explaining cultural borders on Wikipedia through multilingual co-editing activity

“…Several studies delineated cultures by language, and focused on Wikipedia data. In particular, Laufer and colleagues [33] developed measures of cultural similarity, understanding, and affinity through comparing how food cultures are described by selfand foreign communities. Eom et al [34] applied ranking algorithms to biographical articles and obtained a network of cultural agreement on what historical figures are viewed as important, which includes 24 language points of view.…”

Dynamics and biases of online attention: the case of aircraft crashes

“…The high response rate and pace of coverage in Wikipedia in relation to breaking news [13,14] is another feature that makes Wikipedia a good research platform to address questions related to collective attention. For instance, researchers have analysed Wikipedia edit records to identify and model the most controversial topics in different languages [15,16], to study the European food culture [17] and to highlight entanglement of cultures by ranking historical figures [18]. Wikipedia traffic data have also been used to predict movie box office revenues [19], stock market moves [20], electoral popularity [21] and influenza outbreaks [22,23].…”

Population Preferences Through Wikipedia Edits

“…Both limitations are present in all currentstate-of-the-art studies. However, it is still possible to gather relevant information from this data source despite such limitations [11], [12].…”