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Climate change has become one of the biggest challenges of our time. Socialmedia platforms such as Twitter play an important role in raising publicawareness and spreading knowledge about the dangers of the current climatecrisis. With the increasing number of campaigns and communication about climatechange through social media, the information could create more awareness andreach the general public and policy makers. However, these Twittercommunications 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 onTwitter and thus classifies the stance of the tweet into one of the twoattitudes towards climate change (denier/believer). The sentimental aspects ofTwitter data on climate change are deeply rooted in general public attitudestoward climate change. Therefore, our work focuses on learning two closelyrelated tasks: Stance Detection and Sentiment Analysis of climate changetweets. We propose a multi-task framework that performs stance detection(primary task) and sentiment analysis (auxiliary task) simultaneously. Theproposed model incorporates the feature-specific and shared-specific attentionframeworks to fuse multiple features and learn the generalized features forboth tasks. The experimental results show that the proposed framework increasesthe performance of the primary task, i.e., stance detection by benefiting fromthe auxiliary task, i.e., sentiment analysis compared to its uni-modal andsingle-task variants.

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