  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 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.
