Political Discourse Analysis

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The field examines political language in settings such as parliamentary speeches, election debates, news, and social media to understand how actors express positions, frame issues, argue, and appeal to emotion. Researchers use supervised language models and domain-specific encoders, large language models for annotation and extraction, and topic, network, and temporal methods to study stance, ideology, rhetoric, agendas, and polarization. These tools make it possible to analyze large multilingual and historical collections, but performance can vary across topics, languages, platforms, and communication styles. A central challenge is ensuring that computational measures capture context and pragmatic nuance without reproducing annotation bias or mistaking association for political effect.

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67 papers9 papers in the last four weeks, down 31% on the four weeks before. 0.1% of all new papers.

Privacy Preserving Topic-wise Sentiment Analysis of the Iran Israel USA Conflict Using Federated Transformer Models
Mar 13, 2026Md Saiful Islam, Tanjim Taharat Aurpa, Sharad Hasan +1Sentiment Analysis Social Media Analysis
Department of Educational Technology and Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh. · Department of Data Science and Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh. · Department of IOT and Robotics Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh.
Classifying Problem and Solution Framing in Congressional Social Media
Mar 10, 2026Misha Melnyk, Mitchell Dolny, Joshua D. Elkind +6Political Discourse Analysis Text Classification
David R. Cheriton School of Computer Science, University of Waterloo · Martin School of Public Policy & Admin, University of Kentucky · Nistler College of Business and Public Administration, University of North Dakota
Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction
Mar 7, 2026Özgür Togay, Javier Garcia-Bernardo, Florian Kunneman +1Stance Detection Political Discourse Analysis
Department of Methodology and Statistics, Utrecht University · Department of Languages, Literature and Communication, Utrecht University
KCLarity at SemEval-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection
Mar 6, 2026Archie Sage, Salvatore GrecoPolitical Discourse Analysis Zero-Shot Text Classification
King’s College London
Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification
Feb 18, 2026Taja Kuzman Pungeršek, Peter Rupnik, Daniela Širinić +1Political Discourse Analysis LLM-Assisted Annotation
∗Jožef Stefan Institute; ∗†‡Ljubljana, Slovenia · §Faculty of Political Science, University of Zagreb; §Zagreb, Croatia · †Faculty of Computer and Information Science, University of Ljubljana; ∗†‡Ljubljana, Slovenia +1
Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising
Oct 16, 2025Alexander Brady, Tunazzina IslamSocial Media Analysis Political Discourse Analysis
Department of Computer Science, ETH Zürich, Zurich, Switzerland · Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
RooseBERT: A New Deal For Political Language Modelling
Aug 5, 2025Deborah Dore, Elena Cabrio, Serena VillataLanguage Model Pretraining Argument Mining
Universit´e Cˆote d’Azur CNRS, INRIA, I3S Sophia Antipolis, France

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Privacy Preserving Topic-wise Sentiment Analysis of the Iran Israel USA Conflict Using Federated Transformer Models

Md Saiful Islam, Tanjim Taharat Aurpa, Sharad Hasan, Farzana Akter

Department of Educational Technology and Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh. · Department of Data Science and Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh. · Department of IOT and Robotics Engineering, University of Frontier Technology, Bangladesh (UFTB), Gazipur, Bangladesh.

Sentiment Analysis Social Media Analysis Political Discourse Analysis

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The recent escalation of the Iran Israel USA conflict in 2026 has triggered widespread global discussions across social media platforms. As people increasingly use these platforms for expressing opinions, analyzing public sentiment from these discussions can provide valuable insights into global public perception. This study aims to analyze global public sentiment regarding the Iran Israel USA conflict by mining user-generated comments from YouTube news channels. The work contributes to public opinion analysis by introducing a privacy preserving framework that combines topic wise sentiment analysis with modern deep learning techniques and Federated Learning. To achieve this, approximately 19,000 YouTube comments were collected from major international news channels and preprocessed to remove noise and normalize text. Sentiment labels were initially generated using the VADER sentiment analyzer and later validated through manual inspection to improve reliability. Latent Dirichlet Allocation (LDA) was applied to identify key discussion topics related to the conflict. Several transformer-based models, including BERT, RoBERTa, XLNet, DistilBERT, ModernBERT, and ELECTRA, were fine tuned for sentiment classification. The best-performing model was further integrated into a federated learning environment to enable distributed training by preserving user data privacy. Additionally, Explainable Artificial Intelligence (XAI) techniques using SHAP were applied to interpret model predictions and identify influential words affecting sentiment classification. Experimental results demonstrate that transformer models perform effectively, and among them, ELECTRA achieved the best performance with 91.32% accuracy. The federated learning also maintained strong performance while preserving privacy, achieving 89.59% accuracy in a two client configuration.