We present an approach to estimating concept drift in online news. Our method is to construct temporal concept vectors from topicannotated news articles, and to correlate the distance between the temporal concept vectors with edits to the Wikipedia entries of the concepts. We find improvements in the correlation when we split the news articles based on the amount of articles mentioning a concept, instead of calendar-based units of time.

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CEUR Workshop Proceedings
1st International Workshop on Detection, Representation and Management of Concept Drift in Linked Open Data (Drift-a-LOD)
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Becher, O.L, Hollink, L, & Elliott, D. (2017). Exploring concept representations for concept drift detection. In SEMANTiCS-WS 2017: Workshops of SEMANTiCS 2017.