# SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media

arXiv: https://arxiv.org/abs/2403.05216 · HTML: https://mlsanity.com/paper/2403.05216/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [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)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://mlsanity.com/paper/1909.08402/) (2019)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://mlsanity.com/paper/1804.07581/) (2018)
- [Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations](https://mlsanity.com/paper/1609.09028/) (2016)
- [A Longitudinal Assessment of the Persistence of Twitter Datasets](https://mlsanity.com/paper/1709.09186/) (2017)
- [Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://mlsanity.com/paper/2206.13214/) (2022)
- [Opinions are Made to be Changed: Temporally Adaptive Stance Classification](https://mlsanity.com/paper/2108.12476/) (2021)
- [Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions](https://mlsanity.com/paper/2109.00475/) (2021)

## Built on by (most cited first, 1 of 1)

- [Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation](https://mlsanity.com/paper/2404.14339/) (2024)

## Abstract (arXiv)

Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target. Our work advances research in few-shot stance detection by introducing SocialPET, a socially informed approach to leveraging language models for the task. Our proposed approach builds on the Pattern Exploiting Training (PET) technique, which addresses classification tasks as cloze questions through the use of language models. To enhance the approach with social awareness, we exploit the social network structure surrounding social media posts. We prove the effectiveness of SocialPET on two stance datasets, Multi-target and P-Stance, outperforming competitive stance detection models as well as the base model, PET, where the labeled instances for the target under study is as few as 100. When we delve into the results, we observe that SocialPET is comparatively strong in identifying instances of the `against' class, where baseline models underperform.