# Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media

arXiv: https://arxiv.org/abs/2305.13047 · HTML: https://mlsanity.com/paper/2305.13047/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Can we trust the evaluation on ChatGPT?](https://mlsanity.com/paper/2303.12767/) (2023)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark](https://mlsanity.com/paper/2304.11085/) (2023)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [EstBERT: A Pretrained Language-Specific BERT for Estonian](https://mlsanity.com/paper/2011.04784/) (2020)
- [Evaluation of contextual embeddings on less-resourced languages](https://mlsanity.com/paper/2107.10614/) (2021)
- [Unraveling Social Perceptions & Behaviors towards Migrants on Twitter](https://mlsanity.com/paper/2112.06642/) (2021)

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

- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023)
- [Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification](https://mlsanity.com/paper/2407.17688/) (2024)
- [Chain of Stance: Stance Detection with Large Language Models](https://mlsanity.com/paper/2408.04649/) (2024)
- [Developing a Natural Language Understanding Model to Characterize Cable News Bias](https://mlsanity.com/paper/2310.09166/) (2023)
- [DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model](https://mlsanity.com/paper/2403.03334/) (2024)
- [Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content](https://mlsanity.com/paper/2505.23812/) (2025)
- [Journalism-Guided Agentic In-Context Learning for News Stance Detection](https://mlsanity.com/paper/2507.11049/) (2025)
- [Multilingual Target-Stance Extraction](https://mlsanity.com/paper/2510.22334/) (2025)
- [LASTIST: LArge-Scale Target-Independent STance dataset](https://mlsanity.com/paper/2510.25783/) (2025)
- [Measuring Social Media Polarization Using Large Language Models and Heuristic Rules](https://mlsanity.com/paper/2601.00927/) (2026)
- [PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media](https://mlsanity.com/paper/2601.03860/) (2026)
- [TruthStance: An Annotated Dataset of Conversations on Truth Social](https://mlsanity.com/paper/2602.14406/) (2026)

## Abstract (arXiv)

Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration. If the approach works in this case, it can be expected to perform as well or better in less demanding scenarios. We annotate a large set of pro and anti-immigration examples, and compare the performance of multiple language models as supervised learners. We also probe the usability of ChatGPT as an instructable zero-shot classifier for the same task. Supervised achieves acceptable performance, and ChatGPT yields similar accuracy. This is promising as a potentially simpler and cheaper alternative for text classification tasks, including in lower-resource languages. We further use the best-performing model to investigate diachronic trends over seven years in two corpora of Estonian mainstream and right-wing populist news sources, demonstrating the applicability of the approach for news analytics and media monitoring settings, and discuss correspondences between stance changes and real-world events.