# MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

arXiv: https://arxiv.org/abs/1606.03784 · HTML: https://mlsanity.com/paper/1606.03784/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2016.

## Built on by (most cited first, 27 of 27)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://mlsanity.com/paper/2208.08797/) (2022)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection](https://mlsanity.com/paper/2109.08113/) (2021)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis](https://mlsanity.com/paper/2102.02111/) (2021)
- [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://mlsanity.com/paper/2111.01340/) (2021)
- [Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit](https://mlsanity.com/paper/2201.11155/) (2022)
- [ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language](https://mlsanity.com/paper/2204.13979/) (2022)
- [Stance Detection on Social Media with Fine-Tuned Large Language Models](https://mlsanity.com/paper/2404.12171/) (2024)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [A Weakly Supervised Approach for Classifying Stance in Twitter Replies](https://mlsanity.com/paper/2103.07098/) (2021)
- [Guiding Computational Stance Detection with Expanded Stance Triangle Framework](https://mlsanity.com/paper/2305.19845/) (2023)
- [Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation](https://mlsanity.com/paper/2404.14339/) (2024)
- [Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation](https://mlsanity.com/paper/2601.19802/) (2026)
- [Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street](https://mlsanity.com/paper/2203.08694/) (2022)
- [Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection](https://mlsanity.com/paper/2311.04495/) (2023)
- [Multilingual Target-Stance Extraction](https://mlsanity.com/paper/2510.22334/) (2025)
- [StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting](https://mlsanity.com/paper/2607.24191/) (2026)
- [From Two Passes to One: Compact and Efficient Target-Stance Extraction](https://mlsanity.com/paper/2609.06108/) (2026)

## Abstract (arXiv)

We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine-tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.