# CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning

arXiv: https://arxiv.org/abs/2404.17609 · HTML: https://mlsanity.com/paper/2404.17609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Visual Recognition with Deep Nearest Centroids](https://mlsanity.com/paper/2209.07383/) (2022)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Universal Text Representation from BERT: An Empirical Study](https://mlsanity.com/paper/1910.07973/) (2019)
- [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://mlsanity.com/paper/2211.03061/) (2022)
- [STANCY: Stance Classification Based on Consistency Cues](https://mlsanity.com/paper/1910.06048/) (2019)
- [TAN-NTM: Topic Attention Networks for Neural Topic Modeling](https://mlsanity.com/paper/2012.01524/) (2020)
- [MSynFD: Multi-hop Syntax aware Fake News Detection](https://mlsanity.com/paper/2402.14834/) (2024)
- [Few-Shot Stance Detection via Target-Aware Prompt Distillation](https://mlsanity.com/paper/2206.13214/) (2022)
- [Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings](https://mlsanity.com/paper/2301.04535/) (2023)
- [Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector](https://mlsanity.com/paper/2312.11023/) (2023)
- [KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://mlsanity.com/paper/2302.12126/) (2023)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees](https://mlsanity.com/paper/2304.13895/) (2023)

## 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)

Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic. Current advanced neural models for stance detection typically employ fully parametric softmax classifiers. However, these methods suffer from several limitations, including lack of explainability, insensitivity to the latent data structure, and unimodality, which greatly restrict their performance and applications. To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection. During training, we construct a heterogeneous graph to structurally organize texts and stances through implicit topics via employing latent Dirichlet allocation. We then perform contrastive graph learning to learn heterogeneous node representations, aggregating informative multi-hop collaborative signals via an elaborate Collaboration Propagation Aggregation (CPA) module. During inference, we introduce a hybrid similarity scoring module to enable the comprehensive incorporation of topic-aware semantics and collaborative signals for stance detection. Extensive experiments on two benchmark datasets demonstrate the state-of-the-art detection performance of CoSD, verifying the effectiveness and explainability of our collaborative framework.