# Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models

arXiv: https://arxiv.org/abs/2210.05715 · HTML: https://mlsanity.com/paper/2210.05715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [Multilingual Stance Detection: The Catalonia Independence Corpus](https://mlsanity.com/paper/2004.00050/) (2020)
- [You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP](https://mlsanity.com/paper/1909.00412/) (2019)
- [Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey](https://mlsanity.com/paper/2005.09649/) (2020)
- [Siamese CBOW: Optimizing Word Embeddings for Sentence Representations](https://mlsanity.com/paper/1606.04640/) (2016)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [Towards Explainable Fact Checking](https://mlsanity.com/paper/2108.10274/) (2021)
- [#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection](https://mlsanity.com/paper/2007.14936/) (2020)
- [QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs](https://mlsanity.com/paper/2011.01181/) (2020)

## Built on by (most cited first, 2 of 2)

- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Political Leaning Inference through Plurinational Scenarios](https://mlsanity.com/paper/2406.07964/) (2024)

## Abstract (arXiv)

The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on the text itself but on the interaction data available on social networks. More specifically, we propose a new method to leverage social information such as friends and retweets by generating Relational Embeddings, namely, dense vector representations of interaction pairs. Our experiments on seven publicly available datasets and four different languages (Basque, Catalan, Italian, and Spanish) show that combining our relational embeddings with discriminative textual methods helps to substantially improve performance, obtaining state-of-the-art results for six out of seven evaluation settings, outperforming strong baselines based on Large Language Models, or other popular interaction-based approaches such as DeepWalk or node2vec.