An Algorithm to Detect Variations in Writing Styles of Columnists After Major Political Changes

Writers tend to follow a certain style that can be detected or at least sketched by an appropriate algorithm. Columnists in newspapers, being also writers, follow their specific style. The style tends to be stable once writers reach maturity, but it is subject to change when internal or external circumstances differ. Here, we apply a bag-of-words approach to approximate the style of several journalists working in Mexican newspapers, and we track their style for a long period of time with the aim of detecting changes when external circumstances, in particular political ones, change. This provided us with an environment for detecting variations in stylomics, which is the closest we can get to an experiment. In particular, we collected hundreds of writings of ten Mexican columnists from different newspapers, both previous to the Presidential Mexican elections of 2018 and posterior to it. We processed these documents on different supervised and not supervised learning algorithms, such as random forest, principal component analysis, and k-means. Likewise, we implemented different validation procedures. As a result, we detected that the style in all studied columnists suffered tangible changes in the frequency of use of some particular words, particularly at specific times, some of which may be related to the 2018 Mexican presidential elections.