# Stance Prediction for Contemporary Issues: Data and Experiments

arXiv: https://arxiv.org/abs/2006.00052 · HTML: https://mlsanity.com/paper/2006.00052/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 7 of 7)

- [CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations](https://mlsanity.com/paper/2310.11501/) (2023)
- [MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection](https://mlsanity.com/paper/2512.04492/) (2025)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning](https://mlsanity.com/paper/2310.07093/) (2023)
- [UstanceBR: a social media language resource for stance prediction](https://mlsanity.com/paper/2312.06374/) (2023)
- [Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features](https://mlsanity.com/paper/2602.22846/) (2026)
- [Structured Claim-Level Discourse Representations for Dense Health Narratives](https://mlsanity.com/paper/2609.18905/) (2026)

## Abstract (arXiv)

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.