# Stance Detection in Web and Social Media: A Comparative Study

arXiv: https://arxiv.org/abs/2007.05976 · HTML: https://mlsanity.com/paper/2007.05976/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 25 of 25)

- [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)
- [Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media](https://mlsanity.com/paper/2305.13047/) (2023)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit](https://mlsanity.com/paper/2201.11155/) (2022)
- [Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance](https://mlsanity.com/paper/2403.00236/) (2024)
- [ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language](https://mlsanity.com/paper/2204.13979/) (2022)
- [Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration](https://mlsanity.com/paper/2402.14296/) (2024)
- [Collaborative Knowledge Infusion for Low-resource Stance Detection](https://mlsanity.com/paper/2403.19219/) (2024)
- [NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action](https://mlsanity.com/paper/2105.05621/) (2021)
- [Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse](https://mlsanity.com/paper/2202.09445/) (2022)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020](https://mlsanity.com/paper/2303.12029/) (2023)
- [Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets](https://mlsanity.com/paper/2204.10710/) (2022)
- [Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models](https://mlsanity.com/paper/2210.05715/) (2022)
- [Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis](https://mlsanity.com/paper/2302.02813/) (2023)
- [Guiding Computational Stance Detection with Expanded Stance Triangle Framework](https://mlsanity.com/paper/2305.19845/) (2023)
- [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)
- [Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction](https://mlsanity.com/paper/2603.23531/) (2026)
- [Distant finetuning with discourse relations for stance classification](https://mlsanity.com/paper/2204.12693/) (2022)
- [Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter](https://mlsanity.com/paper/2207.11500/) (2022)
- [Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection](https://mlsanity.com/paper/2311.04495/) (2023)
- [PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection](https://mlsanity.com/paper/2607.04690/) (2026)

## Abstract (arXiv)

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.