Political Ideology Detection

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10 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Author-Informed Stance Detection: A Dataset and Findings
Oct 28, 2026Ogen Schlachet Drukerman, Einat MinkovPolitical Ideology Detection Stance Detection
Faculty of Computer & Information Science, University of Haifa
Right-Wing Rock or Just Rock? A Computational Linguistic Analysis of Frei.Wild
Sep 30, 2026Carlotta Schneeberger, Kevin TangPolitical Ideology Detection Political Discourse Analysis
Heinrich Heine University Düsseldorf · Faculty of Arts and Humanities, Heinrich Heine University Düsseldorf 1Institute of Linguistics · University of Florida +2
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
Sep 8, 2026Luka Debevc, Nishan Chatterjee, Antoine Doucet +2Multilingual Language Model Evaluation Political Bias in Language Models
1Jožef Stefan Institute, Ljubljana, Slovenia · University of Ljubljana, Ljubljana, Slovenia · 2Jožef Stefan International Postgraduate School, Ljubljana, Slovenia +1
Enhancing LLMs in Predictive Political QA with Semi-Structured Data
Aug 21, 2026Yinan Liu, Zihan Zhou, Zichun Jin +3Political Ideology Detection Question Answering
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
Aug 18, 2026Yijie Xu, Chao Wang, Hui XiongPolitical Ideology Detection Social Media Analysis
Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, China
Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
Jun 4, 2026Upasana ChatterjeePolitical Ideology Detection LLM-Assisted Annotation
Department of Computer Science Columbia University
Ideology Prediction of German Political Texts
May 14, 2026Sinclair Schneider, Florian Steuber, Joao A. G. Schneider +1Transformer Political Discourse Analysis
Bundeswehr University Munich · Research Institute CODE · Carl-Wery-Straße 22 +1
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
May 1, 2026Ta Thanh Thuy, Jiaqi Zhu, Xuan Liu +6Social Media Analysis Heterogeneous GNNs
Nanyang Technological University · NVIDIA · Nanjing University +3
POLAR:A Per-User Association Test in Embedding Space
Mar 16, 2026Pedro Bento, Arthur Buzelin, Arthur Chagas +7Political Ideology Detection Social Bot Detection
Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
LLMs Can Infer Political Alignment from Online Conversations
Mar 11, 2026Byunghwee Lee, Sangyeon Kim, Filippo Menczer +3Privacy Leakage in Language Models Political Ideology Detection
School of Data Science, University of Virginia, Charlottesville, Virginia, USA, 22903 · Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering Indiana University, Bloomington, Indiana, USA, 47408 · Division of Communication and Media, Ewha Womans University, Seoul, Republic of Korea, 03760

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Author-Informed Stance Detection: A Dataset and Findings

Ogen Schlachet Drukerman, Einat Minkov

Faculty of Computer & Information Science, University of Haifa

Political Ideology Detection Stance Detection Large Language Model-Based Text Classification

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Stance detection in social media is challenging, as interpreting a text often requires understanding the author's perspective. We re-frame political stance detection as an author-informed text understanding task, where it is desired to infer both the author's political leaning and their stance towards the target from the text. We first show that human annotators struggle to infer authors' political leanings and stances from individual tweets, and that pretrained large language models (LLMs) perform below human level in this setting. We then demonstrate that author context improves stance detection for both humans and LLMs, with particularly pronounced benefits for tweets perceived as sarcastic. Through extensive experiments, we further show that LLMs can be effectively fine-tuned to predict authors' political affiliations, and that author-informed fine-tuning yields better stance detection performance than standard fine-tuning. This approach not only surpasses human performance but also improves generalization across datasets. To support further research on author-informed political stance detection, we release a large dataset of stance-labeled tweets enriched with author-context information.