Between news and history: identifying networked topics of collective attention on Wikipedia

Gildersleve, Patrick, Lambiotte, Renaud and Yasseri, Taha (2023) Between news and history: identifying networked topics of collective attention on Wikipedia. Journal of Computational Social Science, 6 (2). 845 - 875. ISSN 2432-2717

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Abstract

The digital information landscape has introduced a new dimension to understanding how we collectively react to new information and preserve it at the societal level. This, together with the emergence of platforms such as Wikipedia, has challenged traditional views on the relationship between current events and historical accounts of events, with an ever-shrinking divide between “news” and “history”. Wikipedia’s place as the Internet’s primary reference work thus poses the question of how it represents both traditional encyclopaedic knowledge and evolving important news stories. In other words, how is information on and attention towards current events integrated into the existing topical structures of Wikipedia? To address this, we develop a temporal community detection approach towards topic detection that takes into account both short term dynamics of attention as well as long term article network structures. We apply this method to a dataset of one year of current events on Wikipedia to identify clusters of Wikipedia articles related to news events, distinct from those that would be found solely from page view time series correlations or static network structure. We are able to resolve the topics that more strongly reflect unfolding current events vs more established knowledge by the relative importance of collective attention dynamics vs link structures. We also offer important developments by identifying and describing the emergent topics on Wikipedia. This work provides a means of distinguishing how these information and attention clusters are related to Wikipedia’s twin faces of encyclopaedic knowledge and current events—crucial to understanding the production and consumption of knowledge in the digital age.

Item Type:	Article
Official URL:	https://link.springer.com/journal/42001
Additional Information:	© 2023 The Author(s)
Divisions:	Methodology
Subjects:	H Social Sciences > HE Transportation and Communications H Social Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	04 Jul 2023 09:48
Last Modified:	25 Apr 2024 19:00
URI:	http://eprints.lse.ac.uk/id/eprint/119625

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