Abstract
Presidential debates are one of the most salient moments of a presidential campaign, where candidates are challenged to discuss the main contemporary and historical issues in a country. These debates represent a natural ground for argumentative analysis, which has been always employed to investigate political discourse structure in philosophy and linguistics. In this paper, we take the challenge to analyse these debates from the topic modeling and framing perspective, to enrich the investigation of these data. Our contribution is threefold: first, we apply transformer-based language models (i.e., BERT and RoBERTa) to the classification of generic frames showing that these models improve the results presented in the literature for frame identification; second, we investigate the task of topic modelling in political arguments from the U.S. presidential campaign debates, applying an unsupervised machine learning approach; and finally, we discuss various visualisations of the identified topics and frames from these U.S. presidential election debates to allow a further interpretation of such data.
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Notes
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C stands for cluster.
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For more details, see https://github.com/dallascard/media_frames_corpus.
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Acknowledgments
This work was partly supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. This work was partly supported also by EU Horizon 2020 project AI4Media, under contract no. 951911 (https://ai4media.eu/), and MIREL (http://www.mirelproject.eu/), under contract no. 690974. Shohreh Haddadan hereby acknowledges that this research is supported by the Luxembourg National Research Fund (FNR) (10929115).
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Haddadan, S., Cabrio, E., Soto, A.J., Villata, S. (2023). Topic Modelling and Frame Identification for Political Arguments. In: Dovier, A., Montanari, A., Orlandini, A. (eds) AIxIA 2022 – Advances in Artificial Intelligence. AIxIA 2022. Lecture Notes in Computer Science(), vol 13796. Springer, Cham. https://doi.org/10.1007/978-3-031-27181-6_19
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