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.

翻译：数字信息图景为我们理解社会层面如何集体响应新信息并加以保存引入了新的维度。随着维基百科等平台的出现，这一趋势挑战了关于时事与历史事件记录间关系的传统观点，使"新闻"与"历史"的界限日益模糊。维基百科作为互联网主要参考资料的地位，引发了它如何同时呈现传统百科全书知识与重要新闻事件演变的问题。换言之，关于当前事件的信息及其关注度是如何被整合到维基百科现有的主题结构中的？为解答此问题，我们提出一种考虑短期关注动态与长期文章网络结构的时间性社区检测方法，并将其应用于涵盖维基百科一年内时事的数数据集，以识别出单纯依靠页面浏览时间序列相关性或静态网络结构无法发现的聚类。通过比较集体关注动态与链接结构的相对重要性，我们能够区分那些更强烈反映事态演变进程与更偏重既有知识的主题。我们还通过识别和描述维基百科上的新兴主题，提供了重要进展。本研究提供了一种区分这些信息与关注聚类如何关联维基百科百科全书知识与时事双重面向的方法——这对于理解数字时代知识的产生与消费至关重要。