# Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations

arXiv: https://arxiv.org/abs/2006.00691 · HTML: https://mlsanity.com/paper/2006.00691/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://mlsanity.com/paper/2403.11145/) (2024)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis](https://mlsanity.com/paper/2302.02813/) (2023)
- [DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model](https://mlsanity.com/paper/2403.03334/) (2024)
- [USDC: A Dataset of underlineUser underlineStance and underlineDogmatism in Long underlineConversations](https://mlsanity.com/paper/2406.16833/) (2024)
- [A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media](https://mlsanity.com/paper/2410.08900/) (2024)
- [Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension](https://mlsanity.com/paper/2210.14456/) (2022)
- [PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection](https://mlsanity.com/paper/2511.12130/) (2025)
- [TruthStance: An Annotated Dataset of Conversations on Truth Social](https://mlsanity.com/paper/2602.14406/) (2026)
- [MMDS-Bench: Benchmarking Multimodal Large Language Models on Dynamic Stance in Social Media Interactions](https://mlsanity.com/paper/2608.30903/) (2026)
- [Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs](https://mlsanity.com/paper/2610.11901/) (2026)

## Abstract (arXiv)

Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring or denying stances, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favors the selection of denial-type responses. This class is expected to be more useful in the identification of rumors and determining antagonistic relationships between users. Moreover, we include many baseline models for learning the stance in conversations and compare the performance of various models. We show that combining data from replies and quotes decreases the accuracy of models indicating that the two modalities behave differently when it comes to stance learning.