Related papers: A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

URL: http://arxiv.org/abs/2211.03533v1
Date: Mon, 7 Nov 2022 13:19:44 GMT
Title: A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets
Authors: Apoorva Upadhyaya, Marco Fisichella, Wolfgang Nejdl
Abstract summary: 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) We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously.
Score: 2.111703012534138
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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