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A Multi-task Model for Emotion and Offensive Aided Stance Detection of Climate Change Tweets

Published: 30 April 2023 Publication History

Abstract

In this work, we address the United Nations Sustainable Development Goal 13: Climate Action by focusing on identifying public attitudes toward climate change on social media platforms such as Twitter. Climate change is threatening the health of the planet and humanity. Public engagement is critical to address climate change. However, climate change conversations on Twitter tend to polarize beliefs, leading to misinformation and fake news that influence public attitudes, often dividing them into climate change believers and deniers. Our paper proposes an approach to classify the attitude of climate change tweets (believe/deny/ambiguous) to identify denier statements on Twitter. Most existing approaches for detecting stances and classifying climate change tweets either overlook deniers’ tweets or do not have a suitable architecture. The relevant literature suggests that emotions and higher levels of toxicity are prevalent in climate change Twitter conversations, leading to a delay in appropriate climate action. Therefore, our work focuses on learning stance detection (main task) while exploiting the auxiliary tasks of recognizing emotions and offensive utterances. We propose a multimodal multitasking framework MEMOCLiC that captures the input data using different embedding techniques and attention frameworks, and then incorporates the learned emotional and offensive expressions to obtain an overall representation of the features relevant to the stance of the input tweet. Extensive experiments conducted on a novel curated climate change dataset and two benchmark stance detection datasets (SemEval-2016 and ClimateStance-2022) demonstrate the effectiveness of our approach.

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  • (2024)A Unified Framework for Analyzing Textual Context and Intent in Social MediaACM Transactions on Intelligent Systems and Technology10.1145/368206415:6(1-25)Online publication date: 29-Jul-2024
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cover image ACM Conferences
WWW '23: Proceedings of the ACM Web Conference 2023
April 2023
4293 pages
ISBN:9781450394161
DOI:10.1145/3543507
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 April 2023

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Author Tags

  1. Twitter
  2. climate change
  3. emotion recognition
  4. offensive language
  5. stance detection

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Niedersächsisches Ministerium für Wissenschaft und Kultur
  • European Commission for the explainable Artificial Intelligence in healthcare Management (xAIM)

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WWW '23
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WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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  • (2024)A Unified Framework for Analyzing Textual Context and Intent in Social MediaACM Transactions on Intelligent Systems and Technology10.1145/368206415:6(1-25)Online publication date: 29-Jul-2024
  • (2024)Harnessing Empathy and Ethics for Relevance Detection and Information Categorization in Climate and COVID-19 TweetsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679937(4091-4095)Online publication date: 21-Oct-2024
  • (2024)Unveiling Climate Drivers via Feature Importance Shift Analysis in New ZealandProceedings of the ACM Web Conference 202410.1145/3589334.3648147(4595-4606)Online publication date: 13-May-2024
  • (2024)Polarization Detection on Social Networks: dual contrastive objectives for Self-supervision2024 IEEE 10th International Conference on Collaboration and Internet Computing (CIC)10.1109/CIC62241.2024.00020(80-89)Online publication date: 28-Oct-2024
  • (2024)Assessing Generative Language Models in Classification Tasks: Performance and Self-evaluation Capabilities in the Environmental and Climate Change DomainNatural Language Processing and Information Systems10.1007/978-3-031-70242-6_29(302-313)Online publication date: 20-Sep-2024
  • (2023)Intensity-valued emotions help stance detection of climate change twitter dataProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/693(6246-6254)Online publication date: 19-Aug-2023

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