Details
| Original language | English |
|---|---|
| Title of host publication | ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 |
| Pages | 3948-3958 |
| Number of pages | 11 |
| ISBN (electronic) | 9781450394161 |
| Publication status | Published - 30 Apr 2023 |
| Event | 2023 World Wide Web Conference, WWW 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023 |
Publication series
| Name | ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 |
|---|
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.
Keywords
- climate change, emotion recognition, offensive language, stance detection, Twitter
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
Sustainable Development Goals
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ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023. 2023. p. 3948-3958 (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Multi-task Model for Emotion and Offensive Aided Stance Detection of Climate Change Tweets
AU - Upadhyaya, Apoorva
AU - Fisichella, Marco
AU - Nejdl, Wolfgang
N1 - Funding Information: This work was partly funded by the SoMeCliCS project under the Volkswagen Stiftung and Niedersächsisches Ministerium für Wis-senschaft und Kultur and by the European Commission for the eXplainable Artificial Intelligence in healthcare Management (xAIM) project, agreement no. INEA/CEF/ICT/A2020/2276680. Funding Information: This work was partly funded by the SoMeCliCS project under the Volkswagen Stiftung and Nieders chsisches Ministerium f r Wissenschaft und Kultur and by the European Commission for the eXplainable Artificial Intelligence in healthcare Management (xAIM) project, agreement no. INEA/CEF/ICT/A2020/2276680
PY - 2023/4/30
Y1 - 2023/4/30
N2 - 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.
AB - 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.
KW - climate change
KW - emotion recognition
KW - offensive language
KW - stance detection
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85159305332&partnerID=8YFLogxK
U2 - 10.1145/3543507.3583860
DO - 10.1145/3543507.3583860
M3 - Conference contribution
AN - SCOPUS:85159305332
T3 - ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
SP - 3948
EP - 3958
BT - ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
T2 - 2023 World Wide Web Conference, WWW 2023
Y2 - 30 April 2023 through 4 May 2023
ER -