Political science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way. Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research. This chapter offers an introduction to such methods drawing examples from political science. Focusing on the areas where the strengths of the methods coincide with challenges in these fields, the chapter first presents an introduction to AI and its core technology - machine learning, with its rapidly developing subfield of deep learning. The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report
KNAW Humanities Cluster（荷兰皇家艺术与科学院人文集群）；European Commission（欧盟委员会）；Koc University（科奇大学）；Virginia Tech（弗吉尼亚理工大学）；National University of Singapore（新加坡国立大学）
Political-LLM: Large Language Models in Political Science
Florida State University（佛罗里达州立大学）；University of Southern California（南加利福尼亚大学）；Louisiana State University（路易斯安那州立大学）；University of Virginia（弗吉尼亚大学）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）；Oxford University（牛津大学）；Lehigh University（理海大学）；Rice University（莱斯大学）；Northwestern University（西北大学）；NVIDIA（英伟达公司）
Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division
Department of Methodology and Statistics（方法学与统计学系）
High Risk of Political Bias in Black Box Emotion Inference Models
Institute of Psychology, Polish Academy of Sciences（波兰科学院心理学研究所）；Stowarzyszenie Otwartej Nauki（开放科学协会）；Institute of Philosophy and Sociology, Polish Academy of Sciences（波兰科学院哲学与社会学研究所）
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Xi’an Jiaotong University（西安交通大学）；University of Washington（华盛顿大学）；Tsinghua University（清华大学）；University of California San Diego（加州大学圣迭戈分校）；University of Technology Sydney（悉尼科技大学）
Measuring the Volatility of the Political agenda in Public Opinion and News Media
University of Exeter（埃克塞特大学）；Oxford Internet Institute（牛津互联网研究院）；University of Oxford（牛津大学）；King’s College London（伦敦国王学院）；Alan Turing Institute（艾伦·图灵研究所）；Meedan
Automated Annotation of Evolving Corpora for Augmenting Longitudinal Network Data: A Framework Integrating Large Language Models and Expert Knowledge
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of International Studies and Institute for Carbon Neutrality, Peking University（北京大学国际关系学院与碳中和研究院）；Analytics Lab for Global Risk Politics, Peking University（北京大学全球风险政治分析实验室）
