# Stance Detection on Tweets: An SVM-based Approach

arXiv: https://arxiv.org/abs/1803.08910 · HTML: https://mlsanity.com/paper/1803.08910/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [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)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit](https://mlsanity.com/paper/2201.11155/) (2022)
- [PAR: Political Actor Representation Learning with Social Context and Expert Knowledge](https://mlsanity.com/paper/2210.08362/) (2022)
- [MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification](https://mlsanity.com/paper/2402.01976/) (2024)
- [Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection](https://mlsanity.com/paper/2503.03172/) (2025)
- [Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities](https://mlsanity.com/paper/2503.03787/) (2025)

## Abstract (arXiv)

Stance detection is a subproblem of sentiment analysis where the stance of the author of a piece of natural language text for a particular target (either explicitly stated in the text or not) is explored. The stance output is usually given as Favor, Against, or Neither. In this paper, we target at stance detection on sports-related tweets and present the performance results of our SVM-based stance classifiers on such tweets. First, we describe three versions of our proprietary tweet data set annotated with stance information, all of which are made publicly available for research purposes. Next, we evaluate SVM classifiers using different feature sets for stance detection on this data set. The employed features are based on unigrams, bigrams, hashtags, external links, emoticons, and lastly, named entities. The results indicate that joint use of the features based on unigrams, hashtags, and named entities by SVM classifiers is a plausible approach for stance detection problem on sports-related tweets.