# Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study

arXiv: https://arxiv.org/abs/2110.07693 · HTML: https://mlsanity.com/paper/2110.07693/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Targeting the Benchmark: On Methodology in Current Natural Language Processing Research](https://mlsanity.com/paper/2007.04792/) (2020)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [A Systematic Review of Reproducibility Research in Natural Language Processing](https://mlsanity.com/paper/2103.07929/) (2021)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics](https://mlsanity.com/paper/2010.14531/) (2020)
- [Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model](https://mlsanity.com/paper/2004.11163/) (2020)

## Built on by (most cited first, 9 of 9)

- [Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction](https://mlsanity.com/paper/2209.08966/) (2022)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization](https://mlsanity.com/paper/2402.01375/) (2024)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
- [Explaining Search Result Stances to Opinionated People](https://mlsanity.com/paper/2309.08460/) (2023)
- [Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study](https://mlsanity.com/paper/2404.03987/) (2024)
- [Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation](https://mlsanity.com/paper/2404.14339/) (2024)
- [Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition](https://mlsanity.com/paper/2209.14780/) (2022)

## Abstract (arXiv)

Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et. al., 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model's performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model's performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.