# Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges

arXiv: https://arxiv.org/abs/2409.13594 · HTML: https://mlsanity.com/paper/2409.13594/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 99 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 30 of 33)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (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)
- [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)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://mlsanity.com/paper/2403.11145/) (2024)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://mlsanity.com/paper/2208.08797/) (2022)
- [The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions](https://mlsanity.com/paper/2406.12480/) (2024)
- [Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning](https://mlsanity.com/paper/2403.14895/) (2024)
- [Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model](https://mlsanity.com/paper/2409.00597/) (2024)
- [A Longitudinal Assessment of the Persistence of Twitter Datasets](https://mlsanity.com/paper/1709.09186/) (2017)
- [Cross-target Stance Detection by Exploiting Target Analytical Perspectives](https://mlsanity.com/paper/2401.01761/) (2024)
- [Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://mlsanity.com/paper/2206.13214/) (2022)
- [Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings](https://mlsanity.com/paper/2301.04535/) (2023)
- [Trump vs. Hillary: What went Viral during the 2016 US Presidential Election](https://mlsanity.com/paper/1707.03375/) (2017)
- [Assessing Sentiment of the Expressed Stance on Social Media](https://mlsanity.com/paper/1908.03181/) (2019)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Zero-Shot Stance Detection using Contextual Data Generation with LLMs](https://mlsanity.com/paper/2405.11637/) (2024)

## Built on by (most cited first, 4 of 4)

- [Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection](https://mlsanity.com/paper/2503.02328/) (2025)
- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue](https://mlsanity.com/paper/2608.08210/) (2026)
- [StanceEval 2026: The Second Stance Detection Shared Task](https://mlsanity.com/paper/2610.03215/) (2026)

## Abstract (arXiv)

Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.