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Staying on the Democratic Script? A Deep Learning Analysis of the Speechmaking of U.S. Presidents

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journal contribution
modified on 2024-04-29, 16:44

Dynamic agenda representation assumes a linkage between the policy emphases prescribed by various democratic inputs (electoral promises and public opinion polls) and policy agendas ranging from the media to executive orders. An extrapolation of this idea would propose that, in the U.S. context, policy emphasis in major programmatic messages such as State of the Union addresses would be followed by the president's day-to-day communication. We investigate this congruence with a new database of presidential speeches that, for the first time, offers a deep learning-enhanced sentence-level policy topic coding of various forms of the speeches U.S. presidents made from Truman to Trump (for a total count of 16,523 speeches divided into nearly 2 million individual sentences). Using this database, we demonstrate that presidents' occasional, day-to-day remarks strongly correlate with the annual policy messages—in this sense, presidents are staying on the democratic script.


The research project was supported by the European Union within the framework of the RRF-2.3.1-21-2022-00004 Artificial Intelligence National Laboratory Program; by the EU Horizon 2020 Framework Programme (H2020) under grant agreement no. 101008468 (SLICES); by the Hungarian Academy of Sciences: V-Shift Momentum Project; and by the Israel Science Foundation (ISF no. 1271/18). We are grateful for the possibility to use the HUN-REN Cloud