# Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content

arXiv: https://arxiv.org/abs/2505.23812 · HTML: https://mlsanity.com/paper/2505.23812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 77 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [SelfDoc: Self-Supervised Document Representation Learning](https://mlsanity.com/paper/2106.03331/) (2021)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation](https://mlsanity.com/paper/2201.08702/) (2022)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://mlsanity.com/paper/2204.03839/) (2022)
- [Stance Detection: A Practical Guide to Classifying Political Beliefs in Text](https://mlsanity.com/paper/2305.01723/) (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)
- [Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey](https://mlsanity.com/paper/2005.09649/) (2020)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers](https://mlsanity.com/paper/1902.10126/) (2019)
- [CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection](https://mlsanity.com/paper/2410.08797/) (2024)
- [Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection](https://mlsanity.com/paper/2010.09078/) (2020)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification](https://mlsanity.com/paper/2504.02615/) (2025)
- [Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel](https://mlsanity.com/paper/1412.4682/) (2014)

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

- [Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Change](https://mlsanity.com/paper/2509.08024/) (2025)

## Abstract (arXiv)

The rapid evolution of social media has generated an overwhelming volume of user-generated content, conveying implicit opinions and contributing to the spread of misinformation. The method aims to enhance the detection of stance where misinformation can polarize user opinions. Stance detection has emerged as a crucial approach to effectively analyze underlying biases in shared information and combating misinformation. This paper proposes a novel method for Stance Prediction through a Label-fused dual cross-Attentive Emotion-aware neural Network (SPLAENet) in misinformative social media user-generated content. The proposed method employs a dual cross-attention mechanism and a hierarchical attention network to capture inter and intra-relationships by focusing on the relevant parts of source text in the context of reply text and vice versa. We incorporate emotions to effectively distinguish between different stance categories by leveraging the emotional alignment or divergence between the texts. We also employ label fusion that uses distance-metric learning to align extracted features with stance labels, improving the method's ability to accurately distinguish between stances. Extensive experiments demonstrate the significant improvements achieved by SPLAENet over existing state-of-the-art methods. SPLAENet demonstrates an average gain of 8.92% in accuracy and 17.36% in F1-score on the RumourEval dataset. On the SemEval dataset, it achieves average gains of 7.02% in accuracy and 10.92% in F1-score. On the P-stance dataset, it demonstrates average gains of 10.03% in accuracy and 11.18% in F1-score. These results validate the effectiveness of the proposed method for stance detection in the context of misinformative social media content.