Challenge: Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses.
Approach: This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities.
Outcome: The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area.

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Challenge: This tutorial will cover the latest techniques and libraries for doing so at each level of analysis.
Approach: This tutorial will cover the latest techniques and libraries for doing so at each level of analysis.
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The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse (2020.findings-emnlp)

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Challenge: Existing computational models of political discourse do not incorporate metaphor and emotion in their functions.
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Theory-Grounded Computational Text Analysis (2023.acl-short)

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Challenge: A broad space separates its two constituent disciplines—natural language processing and social science—which has to date been sidestepped rather than filled by applying increasingly complex computational models to problems in social science research.
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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.
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Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)

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Challenge: 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper.
Approach: This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems.
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Narrative Theory for Computational Narrative Understanding (2021.emnlp-main)

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Challenge: a growing body of theoretical work on narrative has been focused on the field of natural language processing . this position paper aims to provide a unifying framework for the computational study of narrative .
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GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020.coling-main)

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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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Understanding “Democratization” in NLP and ML Research (2024.emnlp-main)

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Challenge: a large number of NLP and ML papers mention terms related to democracy . authors find that democratization is most frequently used to convey (ease of) access to or use of technologies without meaningfully engaging with theories of democratisation.
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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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Event-Centric Natural Language Processing (2021.acl-tutorials)

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Challenge: This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach: This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
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