Papers with political science
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
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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. |
| 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. |
Political Ideology and Polarization: A Multi-dimensional Approach (2022.naacl-main)
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| Challenge: | Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. |
| Approach: | They propose a novel approach for the study of ideology based on its left or right positions on the issue being discussed. |
| Outcome: | The proposed method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. |
An Environment for Relational Annotation of Political Debates (P19-3)
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| Challenge: | Scalable text analysis techniques can open corpora to new questions in computational social sciences and digital humanities. |
| Approach: | They describe a tool that allows annotating newspaper text with rich information about claims (demands) raised by politicians and other actors. |
| Outcome: | The MARDY tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors. |
Llama meets EU: Investigating the European political spectrum through the lens of LLMs (2024.naacl-short)
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| Challenge: | Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. |
| Approach: | They adapt Llama Chat to a European political context and audit its political leanings based on the EUandI questionnaire to analyze its political knowledge and ability to reason in context. |
| Outcome: | The proposed model is adapted from speeches of individual euro-parties from debates in the European Parliament to analyze its political leanings. |
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies (2022.findings-emnlp)
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| Challenge: | Practitioners from many disciplines use expert-crafted taxonomies to make sense of large, unlabeled corpora. |
| Approach: | They propose a weakly supervised algorithm for seeded hierarchical clustering that fits unlabeled data to taxonomies using a small set of labeled examples. |
| Outcome: | The proposed algorithm outperforms baselines on three real-world datasets. |
A Query-Driven Topic Model (2021.findings-acl)
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| Challenge: | Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. |
| Approach: | They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics. |
| Outcome: | The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
”I Never Said That”: A dataset, taxonomy and baselines on response clarity classification (2024.findings-emnlp)
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| Challenge: | Equivocation and ambiguity in public speech are well-studied discourse phenomena . a new taxonomy aims to detect and classify response clarity in political interviews . |
| Approach: | They propose a taxonomy that uses Large Language Models and human annotations to detect and classify response clarity in political interviews. |
| Outcome: | The proposed taxonomy combines ChatGPT and human annotations to identify clarity in political questions . it provides a fine-grained taxonomies for evasion techniques related to unclear, ambiguous responses . |
Analyzing Online Political Advertisements (2021.findings-acl)
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| Challenge: | Online political advertising is an integral part of modern digital election campaigning. |
| Approach: | They propose to use textual and visual information from pre-trained neural models to infer the political ideology of an ad sponsor and identify whether the sponsor is an official political party or a third-party organization. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for generic commercial ad classification and linguistic analysis to study the characteristics of political ads discourse. |
ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence (2022.naacl-main)
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Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, Vito D’Orazio
| Challenge: | Traditionally, researchers used manual coding to track conflict processes worldwide, but the high costs and slow pace of domain experts make it difficult and costly to monitor complex and rapidly changing conflicts. |
| Approach: | They propose a domain-specific pre-trained language model for conflict and political violence that can be used to train a language model from scratch and continue training. |
| Outcome: | The proposed model outperforms BERT in conflict research. |
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis (D19-1)
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| Challenge: | a new study examines the use of labeled and unlabeled corpora in political science research . large corporata often contain documents of a certain subject or type, but they are often unlabed . a recent study found that labeles with pertinent documents stem from a single source . |
| Approach: | They propose an unsupervised domain adaptation framework that uses a text classification model and time-aware training to ensure it works well with diachronic corpora. |
| Outcome: | The proposed framework outperforms benchmarks on an expert-annotated dataset and is more stable and learns better representations. |
On the Relationship between Truth and Political Bias in Language Models (2024.emnlp-main)
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Suyash Fulay, William Brannon, Shrestha Mohanty, Cassandra Overney, Elinor Poole-Dayan, Deb Roy, Jad Kabbara
| Challenge: | Language model alignment research often attempts to ensure that models are helpful and harmless, but can obscure how improving one aspect might impact the other. |
| Approach: | They analyze the relationship between truthfulness and political bias in language models. |
| Outcome: | The results show that optimizing models for truthfulness results in a left-leaning political bias. |
An Attribution Relations Corpus for Political News (L18-1)
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| Challenge: | Existing resources for recognizing attributions in context are limited in size and completeness. |
| Approach: | They propose to use the largest and most complete attribution relations corpus to date . they propose to create sophisticated end-to-end solutions for attribution extraction . |
| Outcome: | The political news attribution relations corpus 2016 is the largest and most complete attribution relations corpuse to date. |
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)
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| Challenge: | Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics. |
| Approach: | They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations. |
| Outcome: | The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. |
Investigating Independence vs. Control: Agenda-Setting in Russian News Coverage on Social Media (2022.lrec-1)
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| Challenge: | a major challenge in the media industry has always been its targeted manipulation, says a new study . agenda-setting is a well-known phenomenon in political science . authors explore the relationship between economic indicators and mentions of foreign geopolitical entities . |
| Approach: | They investigate agenda-setting in the Russian social media landscape . they explore the relation between economic indicators and mentions of foreign geopolitical entities . |
| Outcome: | The authors examine the relationship between economic indicators and mentions of foreign geopolitical entities, as well as of Russia itself. |
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
| Challenge: | SKILL project aims to provide students with AI tools to facilitate analysis of argumentation in scholarly articles on international relations. |
| Approach: | They propose to use AI to analyze argumentation in scholarly articles on international relations . they use a dataset, discourse analysis, and baseline experiments to examine argumentation and domain content types . |
| Outcome: | The proposed method enables educationally-relevant insight into scholarly IR discourse . it requires domain-specific training and fine-tuning on relation and content type prediction tasks. |