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

• URL: http://arxiv.org/abs/2207.00489v1
• Date: Fri, 1 Jul 2022 15:23:23 GMT
• Title: Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data
• Authors: Mykola Makhortykh, Ernesto de Le\'on, Aleksandra Urman, Clara Christner, Maryna Sydorova, Silke Adam, Michaela Maier, and Teresa Gil-Lopez
• Abstract summary: We discuss how these techniques can be used to detect political content across different platforms. We compare the performance of three groups of detection techniques relying on dictionaries, supervised machine learning, or neural networks. 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.
• Score: 48.7576911714538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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