# Examining Political Rhetoric with Epistemic Stance Detection

arXiv: https://arxiv.org/abs/2212.14486 · HTML: https://mlsanity.com/paper/2212.14486/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 123 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Revisiting Few-sample BERT Fine-tuning](https://mlsanity.com/paper/2006.05987/) (2020)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [Handling Divergent Reference Texts when Evaluating Table-to-Text Generation](https://mlsanity.com/paper/1906.01081/) (2019)
- [Fine-Grained Argument Unit Recognition and Classification](https://mlsanity.com/paper/1904.09688/) (2019)
- [Neural models of factuality](https://mlsanity.com/paper/1804.02472/) (2018)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://mlsanity.com/paper/1804.07581/) (2018)
- [Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations](https://mlsanity.com/paper/1609.09028/) (2016)
- [He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://mlsanity.com/paper/2107.00807/) (2021)
- [Fact Checking in Community Forums](https://mlsanity.com/paper/1803.03178/) (2018)
- [Neural-Davidsonian Semantic Proto-role Labeling](https://mlsanity.com/paper/1804.07976/) (2018)
- [Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://mlsanity.com/paper/1907.03227/) (2019)
- ["i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter](https://mlsanity.com/paper/1707.07212/) (2017)

## Built on by (most cited first, 1 of 1)

- [FactAppeal: Identifying Epistemic Factual Appeals in News Media](https://mlsanity.com/paper/2510.10627/) (2025)

## Abstract (arXiv)

Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders -- respected allies and opposed bogeymen -- across U.S. political ideologies.