Difference between revisions of "Analysing Timelines of National Histories Across Wikipedia Editions: a Comparative Computational Approach"
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| − | '''Analysing Timelines of National Histories Across Wikipedia Editions: a Comparative Computational Approach''' - scientific work related to Wikipedia quality published in 2017, written by Anna Samoilenko, Florian Lemmerich, Katrin Weller, Maria Zens and Markus Strohmaier. | + | '''Analysing Timelines of National Histories Across Wikipedia Editions: a Comparative Computational Approach''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Anna Samoilenko]], [[Florian Lemmerich]], [[Katrin Weller]], [[Maria Zens]] and [[Markus Strohmaier]]. |
== Overview == | == Overview == | ||
| − | Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, authors aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on Wikipedia. In particular, authors study articles related to the history of all UN member states and compare them in 30 language editions. Authors 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). Authors also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. Authors hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere. | + | Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, authors aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on [[Wikipedia]]. In particular, authors study articles related to the history of all UN member states and compare them in 30 language editions. Authors 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). Authors also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. Authors hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere. |
Revision as of 21:37, 14 October 2020
Analysing Timelines of National Histories Across Wikipedia Editions: a Comparative Computational Approach - scientific work related to Wikipedia quality published in 2017, written by Anna Samoilenko, Florian Lemmerich, Katrin Weller, Maria Zens and Markus Strohmaier.
Overview
Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, authors aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on Wikipedia. In particular, authors study articles related to the history of all UN member states and compare them in 30 language editions. Authors 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). Authors also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. Authors hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere.
