# Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings

arXiv: https://arxiv.org/abs/2412.03681 · HTML: https://mlsanity.com/paper/2412.03681/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 64 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks](https://mlsanity.com/paper/2301.08774/) (2023)

## Built on by (most cited first, 3 of 3)

- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection](https://mlsanity.com/paper/2604.27934/) (2026)
- [MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection](https://mlsanity.com/paper/2511.06057/) (2025)

## Abstract (arXiv)

Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work we introduce TASTE -- a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pretrained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding -- emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection.