Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates (P19-1)
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| Challenge: | a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making. |
| Approach: | They propose to use a requirements analysis and an annotated pilot corpus of migration claims to build a computationally-based model of political debates from newspaper reports. |
| Outcome: | The proposed framework could be scaled up to a large scale and be useful for political scientists. |
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| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
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| Challenge: | Existing computational models of political discourse do not incorporate metaphor and emotion in their functions. |
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| Challenge: | Parliamentary debates are a valuable language resource for analyzing comprehensive options in a functional, free society. |
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| Challenge: | Scalable text analysis techniques can open corpora to new questions in computational social sciences and digital humanities. |
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Structured Representation Learning for Online Debate Stance Prediction (C18-1)
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Argumentation and Domain Discourse in Scholarly Articles on the Theory of International Relations (2025.coling-main)
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Magdalena Wolska, Sassan Gholiagha, Mitja Sienknecht, Dora Kiesel, Irene Lopez Garcia, Patrick Riehmann, Matti Wiegmann, Bernd Froehlich, Katrin Girgensohn, Jürgen Neyer, Benno Stein
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Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates (P19-1)
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| Challenge: | Political debates are a natural application scenario for Argument Mining. |
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Aligning Large Language Models with Diverse Political Viewpoints (2024.emnlp-main)
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| Challenge: | Large language models such as ChatGPT exhibit striking political biases . a recent study shows that chatbots exhibit progressive, liberal, and proenvironmental biase . |
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