气候变化推文情感辅助姿态检测的多任务模型

Apoorva Upadhyaya, Marco Fisichella, Wolfgang Nejdl
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引用次数: 0

摘要

气候变化已经成为我们这个时代最大的挑战之一。Twitter等社交媒体平台在提高公众意识和传播有关当前气候危机危险的知识方面发挥着重要作用。随着越来越多的关于气候变化的活动和通过社交媒体进行的交流,这些信息可以创造更多的意识,并接触到普通公众和决策者。然而,这些推特上的交流导致了信仰的两极分化,舆论主导的意识形态,并经常分裂成气候变化否认者和信仰者两个群体。在本文中,我们提出了一个框架,有助于识别推特上的否认者陈述,从而将推特的立场分类为对气候变化的两种态度之一(否认者/信徒)。Twitter关于气候变化的数据的情感层面深深植根于公众对气候变化的普遍态度。因此,我们的工作重点是学习两个密切相关的任务:气候变化推文的立场检测和情感分析。我们提出了一个同时执行姿态检测(主任务)和情感分析(辅助任务)的多任务框架。该模型结合了特征特定和共享特定的注意力框架,融合了多个特征,并学习了两个任务的广义特征。实验结果表明,与单模态和单任务变体相比,所提出的框架通过受益于辅助任务(即情感分析)来提高主任务(即姿态检测)的性能。
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A Multi-Task Model for Sentiment Aided Stance Detection of Climate Change Tweets
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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