Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 |
| Editors | Edith Elkind |
| Pages | 6246-6254 |
| Number of pages | 9 |
| ISBN (electronic) | 9781956792034 |
| Publication status | Published - 2023 |
| Event | 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China Duration: 19 Aug 2023 → 25 Aug 2023 |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| Volume | 2023-August |
| ISSN (Print) | 1045-0823 |
Abstract
Our study focuses on the United Nations Sustainable Development Goal 13: Climate Action, by identifying public attitudes on Twitter about climate change. Public consent and participation is the key factor in dealing with climate crises. However, discussions about climate change on Twitter are often influenced by the polarised beliefs that shape the discourse and divide it into communities of climate change deniers and believers. In our work, we propose a framework that helps identify different attitudes in tweets about climate change (deny, believe, ambiguous). Previous literature often lacks an efficient architecture or ignores the characteristics of climate-denier tweets. Moreover, the presence of various emotions with different levels of intensity turns out to be relevant for shaping discussions on climate change. Therefore, our paper utilizes emotion recognition and emotion intensity prediction as auxiliary tasks for our main task of stance detection. Our framework injects the words affecting the emotions embedded in the tweet to capture the overall representation of the attitude in terms of the emotions associated with it. The final task-specific and shared feature representations are fused with efficient embedding and attention techniques to detect the correct attitude of the tweet. Extensive experiments on our novel curated dataset, two publicly available climate change datasets (ClimateICWSM-2023 and ClimateStance-2022), and a benchmark dataset for stance detection (SemEval-2016) validate the effectiveness of our approach.
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Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023. ed. / Edith Elkind. 2023. p. 6246-6254 (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2023-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data
AU - Upadhyaya, Apoorva
AU - Fisichella, Marco
AU - Nejdl, Wolfgang
N1 - Funding Information: This research was partially funded by the SoMeCliCS project under the Volkswagen Stiftung and Niedersächsisches Ministerium für Wissenschaft und Kultur and by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with grant No. 01DD20003.
PY - 2023
Y1 - 2023
N2 - Our study focuses on the United Nations Sustainable Development Goal 13: Climate Action, by identifying public attitudes on Twitter about climate change. Public consent and participation is the key factor in dealing with climate crises. However, discussions about climate change on Twitter are often influenced by the polarised beliefs that shape the discourse and divide it into communities of climate change deniers and believers. In our work, we propose a framework that helps identify different attitudes in tweets about climate change (deny, believe, ambiguous). Previous literature often lacks an efficient architecture or ignores the characteristics of climate-denier tweets. Moreover, the presence of various emotions with different levels of intensity turns out to be relevant for shaping discussions on climate change. Therefore, our paper utilizes emotion recognition and emotion intensity prediction as auxiliary tasks for our main task of stance detection. Our framework injects the words affecting the emotions embedded in the tweet to capture the overall representation of the attitude in terms of the emotions associated with it. The final task-specific and shared feature representations are fused with efficient embedding and attention techniques to detect the correct attitude of the tweet. Extensive experiments on our novel curated dataset, two publicly available climate change datasets (ClimateICWSM-2023 and ClimateStance-2022), and a benchmark dataset for stance detection (SemEval-2016) validate the effectiveness of our approach.
AB - Our study focuses on the United Nations Sustainable Development Goal 13: Climate Action, by identifying public attitudes on Twitter about climate change. Public consent and participation is the key factor in dealing with climate crises. However, discussions about climate change on Twitter are often influenced by the polarised beliefs that shape the discourse and divide it into communities of climate change deniers and believers. In our work, we propose a framework that helps identify different attitudes in tweets about climate change (deny, believe, ambiguous). Previous literature often lacks an efficient architecture or ignores the characteristics of climate-denier tweets. Moreover, the presence of various emotions with different levels of intensity turns out to be relevant for shaping discussions on climate change. Therefore, our paper utilizes emotion recognition and emotion intensity prediction as auxiliary tasks for our main task of stance detection. Our framework injects the words affecting the emotions embedded in the tweet to capture the overall representation of the attitude in terms of the emotions associated with it. The final task-specific and shared feature representations are fused with efficient embedding and attention techniques to detect the correct attitude of the tweet. Extensive experiments on our novel curated dataset, two publicly available climate change datasets (ClimateICWSM-2023 and ClimateStance-2022), and a benchmark dataset for stance detection (SemEval-2016) validate the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=85170400884&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2023/693
DO - 10.24963/ijcai.2023/693
M3 - Conference contribution
AN - SCOPUS:85170400884
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 6246
EP - 6254
BT - Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
A2 - Elkind, Edith
T2 - 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Y2 - 19 August 2023 through 25 August 2023
ER -