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The growing availability of data about online information behaviour enablesnew possibilities for political communication research. However, the volume andvariety of these data makes them difficult to analyse and prompts the need fordeveloping automated content approaches relying on a broad range of naturallanguage processing techniques (e.g. machine learning- or neural network-basedones). In this paper, we discuss how these techniques can be used to detectpolitical content across different platforms. Using three validation datasets,which include a variety of political and non-political textual documents fromonline platforms, we systematically compare the performance of three groups ofdetection techniques relying on dictionaries, supervised machine learning, orneural networks. We also examine the impact of different modes of datapreprocessing (e.g. stemming and stopword removal) on the low-costimplementations of these techniques using a large set (n = 66) of detectionmodels. Our results show the limited impact of preprocessing on modelperformance, with the best results for less noisy data being achieved by neuralnetwork- and machine-learning-based models, in contrast to the more robustperformance of dictionary-based models on noisy data.

Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data