  The growing availability of data about online information behaviour enables
new possibilities for political communication research. However, the volume and
variety of these data makes them difficult to analyse and prompts the need for
developing automated content approaches relying on a broad range of natural
language processing techniques (e.g. machine learning- or neural network-based
ones). In this paper, we discuss how these techniques can be used to detect
political content across different platforms. Using three validation datasets,
which include a variety of political and non-political textual documents from
online platforms, we systematically compare the performance of three groups of
detection techniques relying on dictionaries, supervised machine learning, or
neural networks. We also examine the impact of different modes of data
preprocessing (e.g. stemming and stopword removal) on the low-cost
implementations of these techniques using a large set (n = 66) of detection
models. Our results show the limited impact of preprocessing on model
performance, with the best results for less noisy data being achieved by neural
network- and machine-learning-based models, in contrast to the more robust
performance of dictionary-based models on noisy data.
