This thesis presents a wavelet-based method for detecting moments of fast change in the textual contents of historical newspapers. The method works by generating time series of the relative frequencies of different words in the newspaper contents over time, and calculating their wavelet transforms. Wavelet transform is essentially a group of transformations describing the changes happening in the original time series at different time scales, and can therefore be used to pinpoint moments of fast change in the data. The produced wavelet transforms are then used to detect fast changes in word frequencies by examining products of multiple scales of the transform. The aim of this thesis is to examine the applicability of a wavelet transform-based method for change detection in time series generated from historical newspaper data. The change detection method examined in the thesis was developed as a part of NewsEye, an EU-funded project that aims to provide improved tools and methods for performing historical research using newspaper archives as the source material.