# A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets arXiv: https://arxiv.org/abs/2211.03533 ยท HTML: https://mlsanity.com/paper/2211.03533/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 51 references that are in the map. - First posted on arXiv 7 Nov 2022. ## Builds on (most cited first, 16 of 16) - [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) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022) - [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017) - [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020) - [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://mlsanity.com/paper/1512.08422/) (2015) - [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016) - [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015) - [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019) - [Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection](https://mlsanity.com/paper/1909.01720/) (2019) - [Climate change sentiment on Twitter: An unsolicited public opinion poll](https://mlsanity.com/paper/1505.03804/) (2015) - [Fake News Detection System using XLNet model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task](https://mlsanity.com/paper/2101.11425/) (2021) - [A Computational Analysis of Polarization on Indian and Pakistani Social Media](https://mlsanity.com/paper/2005.09803/) (2020) - [A Semi-Supervised Approach to Detecting Stance in Tweets](https://mlsanity.com/paper/1709.01895/) (2017) - [Polarizing Tweets on Climate Change](https://mlsanity.com/paper/2008.13054/) (2020) ## Built on by (most cited first, 11 of 11) - [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023) - [Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification](https://mlsanity.com/paper/2309.13734/) (2023) - [Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations](https://mlsanity.com/paper/2308.11021/) (2023) - [ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing](https://mlsanity.com/paper/2310.08099/) (2023) - [Enhancing Stance Classification on Social Media Using Quantified Moral Foundations](https://mlsanity.com/paper/2310.09848/) (2023) - [EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection](https://mlsanity.com/paper/2404.05133/) (2024) - [Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change Domain](https://mlsanity.com/paper/2408.17362/) (2024) - [3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection](https://mlsanity.com/paper/2506.19263/) (2025) - [Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change](https://mlsanity.com/paper/2505.18653/) (2025) - [Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Change](https://mlsanity.com/paper/2509.08024/) (2025) - [A Decade of Climate Polarization on Brazilian YouTube using Language Models](https://mlsanity.com/paper/2609.27811/) (2026) ## Abstract (arXiv) Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through social media, the information could create more awareness and reach the general public and policy makers. However, these Twitter communications lead to polarization of beliefs, opinion-dominated ideologies, and often a split into two communities of climate change deniers and believers. In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer). The sentimental aspects of Twitter data on climate change are deeply rooted in general public attitudes toward climate change. Therefore, our work focuses on learning two closely related tasks: Stance Detection and Sentiment Analysis of climate change tweets. We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously. The proposed model incorporates the feature-specific and shared-specific attention frameworks to fuse multiple features and learn the generalized features for both tasks. The experimental results show that the proposed framework increases the performance of the primary task, i.e., stance detection by benefiting from the auxiliary task, i.e., sentiment analysis compared to its uni-modal and single-task variants.