Political Analysis
@polanalysis.bsky.social
Official Journal of the Society for Political Methodology
https://www.cambridge.org/core/journals/political-analysis
https://www.cambridge.org/core/journals/political-analysis
They find that complex prompting strategies can lead to improved model performance. The authors also offer several recommendations for researchers using LLMs for stance detection in political texts. You can read the full paper here: www.cambridge.org/core/journal...
Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Models | Political Analysis | Cambridge Core
Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Models
www.cambridge.org
January 13, 2026 at 5:50 PM
They find that complex prompting strategies can lead to improved model performance. The authors also offer several recommendations for researchers using LLMs for stance detection in political texts. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Model,” Max Griswold, Michael Robbins, and @sociologian.bsky.social evaluate fine-tuning strategies to improve LLM performance using social media data surrounding the 2020 election.
January 13, 2026 at 5:50 PM
Currently in FirstView: In “Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Model,” Max Griswold, Michael Robbins, and @sociologian.bsky.social evaluate fine-tuning strategies to improve LLM performance using social media data surrounding the 2020 election.
The Political Domain Enhanced BERT-based Algorithm for Textual Entailment (DEBATE) is benchmarked against other popular supervised classifiers. Ultimately, DEBATE is both efficient and completely open source. Read the paper here: www.cambridge.org/core/journal...
Political DEBATE: Efficient Zero-Shot and Few-Shot Classifiers for Political Text | Political Analysis | Cambridge Core
Political DEBATE: Efficient Zero-Shot and Few-Shot Classifiers for Political Text
www.cambridge.org
January 6, 2026 at 5:35 PM
The Political Domain Enhanced BERT-based Algorithm for Textual Entailment (DEBATE) is benchmarked against other popular supervised classifiers. Ultimately, DEBATE is both efficient and completely open source. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Political DEBATE: Efficient Zero-Shot and Few-Shot Classifiers for Political Text,” Michael Burnham, Kayla Kahn, Ryan Yang Wang, and Rachel Peng introduce DEBATE, a new open source foundation model for classifying political documents.
January 6, 2026 at 5:35 PM
Currently in FirstView: In “Political DEBATE: Efficient Zero-Shot and Few-Shot Classifiers for Political Text,” Michael Burnham, Kayla Kahn, Ryan Yang Wang, and Rachel Peng introduce DEBATE, a new open source foundation model for classifying political documents.
Reposted by Political Analysis
NEW ISSUE from @polanalysis.bsky.social -
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB
December 31, 2025 at 5:40 PM
NEW ISSUE from @polanalysis.bsky.social -
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB
Their method is demonstrated using social media conversations surrounding the MeToo movement and the 2020 presidential election. This method is an alternative to off-the-shelf methods such as LDA, which are computationally inefficient. Read the full paper here: www.cambridge.org/core/journal...
Analyzing Political Text at Scale with Online Tensor LDA | Political Analysis | Cambridge Core
Analyzing Political Text at Scale with Online Tensor LDA
www.cambridge.org
December 23, 2025 at 5:35 PM
Their method is demonstrated using social media conversations surrounding the MeToo movement and the 2020 presidential election. This method is an alternative to off-the-shelf methods such as LDA, which are computationally inefficient. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Analyzing Political Text at Scale with Online Tensor LDA,” @sarakangaslahti.bsky.social, Danny Ebanks, @jeankossaifi.bsky.social, Anqi Liu, @rmichaelalvarez.bsky.social, and Anima Anandkumar introduce a topic modeling method that scales linearly to billions of documents.
December 23, 2025 at 5:35 PM
Currently in FirstView: In “Analyzing Political Text at Scale with Online Tensor LDA,” @sarakangaslahti.bsky.social, Danny Ebanks, @jeankossaifi.bsky.social, Anqi Liu, @rmichaelalvarez.bsky.social, and Anima Anandkumar introduce a topic modeling method that scales linearly to billions of documents.
They focus on two key political traits, agency and communion, and extract these traits from a large corpus of politicians’ speeches. This approach is validated using human-labeled data and functional tests. You can read the paper here: www.cambridge.org/core/journal...
Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion | Political Analysis | Cambridge Core
Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion
www.cambridge.org
December 18, 2025 at 6:59 PM
They focus on two key political traits, agency and communion, and extract these traits from a large corpus of politicians’ speeches. This approach is validated using human-labeled data and functional tests. You can read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion,” @lukasbirkenmai1.bsky.social and Clemens Lechner introduce an approach to infer politicians’ personality traits from text data.
December 18, 2025 at 6:59 PM
Currently in FirstView: In “Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion,” @lukasbirkenmai1.bsky.social and Clemens Lechner introduce an approach to infer politicians’ personality traits from text data.
They use an image classification task to compare assessments of GenAI models to a national and locally representative survey sample. Overall, GenAI is biased toward national averages over local perspectives. You can read the full paper here: www.cambridge.org/core/journal...
Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception | Political Analysis | Cambridge Core
Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception
www.cambridge.org
December 11, 2025 at 6:05 PM
They use an image classification task to compare assessments of GenAI models to a national and locally representative survey sample. Overall, GenAI is biased toward national averages over local perspectives. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception,” Paige Bollen, @joehigton.bsky.social, and @msands.bsky.social test which populations Generative AI is most representative of.
December 11, 2025 at 6:05 PM
Currently in FirstView: In “Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception,” Paige Bollen, @joehigton.bsky.social, and @msands.bsky.social test which populations Generative AI is most representative of.
They find that survey professionalism is common, but there is limited evidence that survey professionals lower data quality. Professionals do not systematically differ from non-professionals and don’t exhibit more response instability. Read the paper here: www.cambridge.org/core/journal...
Survey Professionalism: New Evidence from Web Browsing Data | Political Analysis | Cambridge Core
Survey Professionalism: New Evidence from Web Browsing Data
www.cambridge.org
December 4, 2025 at 6:05 PM
They find that survey professionalism is common, but there is limited evidence that survey professionals lower data quality. Professionals do not systematically differ from non-professionals and don’t exhibit more response instability. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Survey Professionalism: New Evidence from Web Browsing Data,” Bernhard Clemm von Hohenberg, @tiagoventura.bsky.social, Tiago Ventura, @jonathannagler.bsky.social, @ericka.bric.digital, & Magdalena Wojcieszak provide evidence on survey professionalism across three samples.
December 4, 2025 at 6:05 PM
Currently in FirstView: In “Survey Professionalism: New Evidence from Web Browsing Data,” Bernhard Clemm von Hohenberg, @tiagoventura.bsky.social, Tiago Ventura, @jonathannagler.bsky.social, @ericka.bric.digital, & Magdalena Wojcieszak provide evidence on survey professionalism across three samples.
The plot staircase is introduced as a way of identifying the relative importance of a graph characteristic compared to a baseline. This method is demonstrated using data on economic growth, job creation, and the COVID-19 vaccine rollout. Read the full paper here: www.cambridge.org/core/journal...
Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical Preferences | Political Analysis | Cambridge Core
Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical Preferences
www.cambridge.org
December 2, 2025 at 5:35 PM
The plot staircase is introduced as a way of identifying the relative importance of a graph characteristic compared to a baseline. This method is demonstrated using data on economic growth, job creation, and the COVID-19 vaccine rollout. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical Preferences,” @talbotmandrews.bsky.social, Justin Curl, and Markus Prior examine how visual characteristics influence preferences. They find that people prefer increasing trends.
December 2, 2025 at 5:35 PM
Currently in FirstView: In “Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical Preferences,” @talbotmandrews.bsky.social, Justin Curl, and Markus Prior examine how visual characteristics influence preferences. They find that people prefer increasing trends.
The authors provide a framework to evaluate codebook-LLM measurement, classifying unlabeled documents with LLMs given a human-written codebook. Ultimately, supervised instruction-tuning can substantially improve performance. Read the full paper here: www.cambridge.org/core/journal...
Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts | Political Analysis | Cambridge Core
Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts
www.cambridge.org
November 27, 2025 at 6:05 PM
The authors provide a framework to evaluate codebook-LLM measurement, classifying unlabeled documents with LLMs given a human-written codebook. Ultimately, supervised instruction-tuning can substantially improve performance. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts,” @ahalterman.bsky.social and @katakeith.bsky.social show how “off-the-shelf” LLMs have limitations in faithfully following real-world codebook operationalizations.
November 27, 2025 at 6:05 PM
Currently in FirstView: In “Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts,” @ahalterman.bsky.social and @katakeith.bsky.social show how “off-the-shelf” LLMs have limitations in faithfully following real-world codebook operationalizations.
Bipartite networks are common in social science, but researchers often project data on unipartite networks for analysis. This new model uncovers patterns across node types, uses covariates to explain ties, and fits efficiently. Read the full paper here: www.cambridge.org/core/journal...
A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate | Political Analysis | Cambridge Core
A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate
www.cambridge.org
November 20, 2025 at 6:05 PM
Bipartite networks are common in social science, but researchers often project data on unipartite networks for analysis. This new model uncovers patterns across node types, uses covariates to explain ties, and fits efficiently. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate,” @adelineylo.bsky.social, Santiago Olivella, and Kosuke Imai develop a statistical model of bipartite networks and offer an open-source software package for researchers.
November 20, 2025 at 6:05 PM
Currently in FirstView: In “A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate,” @adelineylo.bsky.social, Santiago Olivella, and Kosuke Imai develop a statistical model of bipartite networks and offer an open-source software package for researchers.
GNNs are advantageous because they can be trained, saved, and deployed on new data, and they can also generate synthetic data. The paper uses the militarized international disputes dataset to illustrate potential applications. Read the paper here: www.cambridge.org/core/journal...
Generative AI and Topological Data Analysis of Longitudinal Panel Data | Political Analysis | Cambridge Core
Generative AI and Topological Data Analysis of Longitudinal Panel Data
www.cambridge.org
November 18, 2025 at 6:11 PM
GNNs are advantageous because they can be trained, saved, and deployed on new data, and they can also generate synthetic data. The paper uses the militarized international disputes dataset to illustrate potential applications. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Generative AI and Topological Data Analysis of Longitudinal Panel Data,” Badredine Arfi constructs an approach to analysing longitudinal panel data which combines topological data analysis and generative AI applied to graph neural networks (GNNs).
November 18, 2025 at 6:11 PM
Currently in FirstView: In “Generative AI and Topological Data Analysis of Longitudinal Panel Data,” Badredine Arfi constructs an approach to analysing longitudinal panel data which combines topological data analysis and generative AI applied to graph neural networks (GNNs).
The package is demonstrated using several political examples where researchers may wish to join messy data. The fuzzylink package outperforms existing methods and even allows researchers to link datasets across languages. You can read the full paper here: www.cambridge.org/core/journal...
Probabilistic Record Linkage Using Pretrained Text Embeddings | Political Analysis | Cambridge Core
Probabilistic Record Linkage Using Pretrained Text Embeddings
www.cambridge.org
November 13, 2025 at 6:05 PM
The package is demonstrated using several political examples where researchers may wish to join messy data. The fuzzylink package outperforms existing methods and even allows researchers to link datasets across languages. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Probabilistic Record Linkage Using Pretrained Text Embeddings,” @joeornstein.bsky.social introduces the R package fuzzylink and shows how to incorporate pretrained text embeddings into probabilistic record linkage procedure.
November 13, 2025 at 6:05 PM
Currently in FirstView: In “Probabilistic Record Linkage Using Pretrained Text Embeddings,” @joeornstein.bsky.social introduces the R package fuzzylink and shows how to incorporate pretrained text embeddings into probabilistic record linkage procedure.
The authors apply the SIR model to data on monthly conflict events between countries, highlighting the model’s ability to illustrate complex influence patterns within networks by linking them to specific covariates. You can read the full paper here: www.cambridge.org/core/journal...
Decomposing Network Influence: Social Influence Regression | Political Analysis | Cambridge Core
Decomposing Network Influence: Social Influence Regression
www.cambridge.org
November 11, 2025 at 5:35 PM
The authors apply the SIR model to data on monthly conflict events between countries, highlighting the model’s ability to illustrate complex influence patterns within networks by linking them to specific covariates. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Decomposing Network Influence: Social Influence Regression,” Shahryar Minhas and Peter Hoff introduce the social influence regression (SIR) model. The SIR model is for relational data that incorporates exogenous covariates into the estimation of influence patterns.
November 11, 2025 at 5:35 PM
Currently in FirstView: In “Decomposing Network Influence: Social Influence Regression,” Shahryar Minhas and Peter Hoff introduce the social influence regression (SIR) model. The SIR model is for relational data that incorporates exogenous covariates into the estimation of influence patterns.