{"type":"ScholarlyArticle","title":"Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data","authors":[{"name":"Apoorva Upadhyaya","slug":"apoorva-upadhyaya","given":"Apoorva","family":"Upadhyaya","affiliation":"Research Center , Leibniz Universität Hannover , Germany"},{"name":"Marco Fisichella","slug":"marco-fisichella","given":"Marco","family":"Fisichella","affiliation":"Research Center , Leibniz Universität Hannover , Germany"},{"name":"Wolfgang Nejdl","slug":"wolfgang-nejdl","given":"Wolfgang","family":"Nejdl","affiliation":"Research Center , Leibniz Universität Hannover , Germany"}],"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.","doi":"10.24963/ijcai.2023/693","url":"https://ijcai.org/proceedings/2023/693","pdf":"https://ijcai.org/proceedings/2023/0693.pdf","markdown":"https://ijcai.org/proceedings/2023/693.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/693","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":693,"pages":{"first":6246,"last":6254},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Natural Language Processing","AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Abro et al., 2022] Hassam Uddin Abro, Zafi Sherhan Shah, and Haseeb Abbasi. Analysis of covid-19 effects on well- being -study of reddit posts using natural language pro- cessing techniques. In 2022 International Conference on Emerging Trends in Electrical, Control, and Telecommu- nication Engineering (ETECTE), pages 1-7, 2022.","title":"Analysis of covid-19 effects on wellbeing -study of reddit posts using natural language processing techniques","authors":"Zafi Sherhan Hassam Uddin Abro, Haseeb Shah, Abbasi","year":"2022","venue":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bahri et al., 2013] Mawardi Bahri, Ryuichi Ashino, and Rémi Vaillancourt. Convolution theorems for quaternion fourier transform: properties and applications. In Abstract and Applied Analysis, volume 2013. Hindawi, 2013.","title":"Convolution theorems for quaternion fourier transform: properties and applications","authors":"Mawardi Bahri, Ryuichi Ashino, Rémi Vaillancourt","year":"2013","venue":"Abstract and Applied Analysis","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Brosch, 2021] Tobias Brosch. Affect and emotions as drivers of climate change perception and action: a review. Current Opinion in Behavioral Sciences, 42:15-21, 2021.","title":"Affect and emotions as drivers of climate change perception and action: a review","authors":"Tobias Brosch, Brosch","year":"2021","venue":"Current Opinion in Behavioral Sciences","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2019] Xingyu Chen, Lei Zou, and Bo Zhao. De- tecting climate change deniers on twitter using a deep neu- ral network. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing, pages 204-210, 2019.","title":"Detecting climate change deniers on twitter using a deep neural network","authors":"Xingyu Chen, Lei Zou, Bo Zhao","year":"2019","venue":"Proceedings of the 2019 11th International Conference on Machine Learning and Computing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Falkenberg et al., 2022] Max Falkenberg, Alessandro Galeazzi, Maddalena Torricelli, Niccolò Di Marco, Francesca Larosa, Madalina Sas, Amin Mekacher, Warren Pearce, Fabiana Zollo, Walter Quattrociocchi, et al. Growing polarization around climate change on social media. Nature Climate Change, pages 1-8, 2022.","title":"Growing polarization around climate change on social media","authors":"Falkenberg","year":"2022","venue":"Nature Climate Change","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fu et al., 2022] Yujie Fu, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, and Jianxing Zheng. Incorporate opinion-towards for stance detection. Knowledge-Based Systems, 246:108657, 2022.","title":"Incorporate opinion-towards for stance detection","authors":"Yujie Fu, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, Jianxing Zheng","year":"2022","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Jang and Hart, 2015] S Mo Jang and P Sol Hart. Polar- ized frames on \"climate change\" and \"global warming\" across countries and states: Evidence from twitter big data. Global Environmental Change, 32:11-17, 2015.","title":"Polarized frames on \"climate change\" and \"global warming\" across countries and states: Evidence from twitter big data","authors":"Jang Mo, P Sol, Hart","year":"2015","venue":"Global Environmental Change","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kabaghe and Qin, 2020] Chuma Kabaghe and Jason Qin. Classifying tweets based on climate change stance. Train- ing, 66(60.9):61, 2020.","title":"Classifying tweets based on climate change stance","authors":"Qin Kabaghe, Chuma Kabaghe, Jason Qin","year":"2020","venue":"Training","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lacoste et al., 2019] Alexandre Lacoste, Alexandra Luc- cioni, Victor Schmidt, and Thomas Dandres. Quantify- ing the carbon emissions of machine learning. CoRR, abs/1910.09700, 2019.","title":"Quantifying the carbon emissions of machine learning","authors":"Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li and Caragea, 2019] Yingjie Li and Cornelia Caragea. Multi-task stance detection with sentiment and stance lex- icons. In Proceedings of the 2019 Conference on Empiri- cal Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 6299-6305, 2019.","title":"Multi-task stance detection with sentiment and stance lexicons","authors":"Yingjie Li, Cornelia Caragea","year":"2019","venue":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Mohammad et al., 2016] Saif Mohammad, Svetlana Kir- itchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on se- mantic evaluation (SemEval-2016), pages 31-41, 2016.","title":"Semeval-2016 task 6: Detecting stance in tweets","authors":"Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, Colin Cherry","year":"2016","venue":"Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mohammad, 2018] Saif M. Mohammad. Word affect in- tensities. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC- 2018), Miyazaki, Japan, 2018.","title":"Word affect intensities","authors":"M Saif, Mohammad","year":"2018","venue":"Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Mou et al., 2015] Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. Natural language in- ference by tree-based convolution and heuristic matching. arXiv preprint arXiv:1512.08422, 2015.","title":"Natural language inference by tree-based convolution and heuristic matching","authors":"Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Nguyen et al., 2020] Dat Quoc Nguyen, Thanh Vu, and Anh Tuan Nguyen. BERTweet: A pre-trained language model for English tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online, October 2020. Associa- tion for Computational Linguistics.","title":"BERTweet: A pre-trained language model for English tweets","authors":"Thanh Dat Quoc Nguyen, Anh Tuan Vu, Nguyen","year":"2020","venue":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pörtner et al., 2022] Hans-O Pörtner, Debra C Roberts, He- len Adams, Carolina Adler, Paulina Aldunce, Elham Ali, Rawshan Ara Begum, Richard Betts, Rachel Bezner Kerr, Robbert Biesbroek, et al. Climate change 2022: Impacts, adaptation and vulnerability. IPCC Geneva, Switzerland, 2022.","title":"Climate change 2022: Impacts, adaptation and vulnerability","authors":"Hans-O Pörtner, Debra C Roberts, Helen Adams, Carolina Adler, Paulina Aldunce, Elham Ali, Rawshan, Ara Begum, Richard Betts, Rachel Bezner Kerr, Robbert Biesbroek","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Qamar et al., 2021] Saira Qamar, Hasan Mujtaba, Hammad Majeed, and Mirza Omer Beg. Relationship identifica- tion between conversational agents using emotion analy- sis. Cognitive Computation, 13:673-687, 2021.","title":"Relationship identification between conversational agents using emotion analysis","authors":"Saira Qamar, Hasan Mujtaba, Hammad Majeed, Omer Mirza, Beg","year":"2021","venue":"Cognitive Computation","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Qin and Ronchieri, 2022] Zhikang Qin and Elisabetta Ronchieri. Exploring pandemics events on twitter by using sentiment analysis and topic modelling. Applied Sciences, 12(23):11924, 2022.","title":"Zhikang Qin and Elisabetta Ronchieri. Exploring pandemics events on twitter by using sentiment analysis and topic modelling","authors":"Ronchieri Qin","year":"2022","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Reveilhac and Schneider, 2023] Maud Reveilhac and Gerold Schneider. Replicable semi-supervised approaches to state-of-the-art stance detection of tweets. Information Processing Management, 60(2):103199, 2023.","title":"Maud Reveilhac and Gerold Schneider. Replicable semi-supervised approaches to state-of-the-art stance detection of tweets","authors":"Schneider Reveilhac","year":"2023","venue":"Information Processing Management","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Salama and Aboukoura, 2018] Sefat Salama and Khalil Aboukoura. Role of emotions in climate change commu- nication. Handbook of Climate Change Communication: Vol. 1: Theory of Climate Change Communication, pages 137-150, 2018.","title":"Role of emotions in climate change communication","authors":"Aboukoura Salama, Sefat Salama, Khalil Aboukoura","year":"2018","venue":"Theory of Climate Change Communication","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shoeb and de Melo, 2020] Abu Awal Md Shoeb and Gerard de Melo. EmoTag1200: Understanding the association be- tween emojis and emotions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Lin- guistics, November 2020.","title":"EmoTag1200: Understanding the association between emojis and emotions","authors":"De Shoeb, ; Abu Melo, Md Awal, Gerard Shoeb, Melo De","year":"2020","venue":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sobhani et al., 2016] Parinaz Sobhani, Saif Mohammad, and Svetlana Kiritchenko. Detecting stance in tweets and analyzing its interaction with sentiment. In Proceedings of the fifth joint conference on lexical and computational semantics, pages 159-169, 2016.","title":"Detecting stance in tweets and analyzing its interaction with sentiment","authors":"Sobhani","year":"2016","venue":"Proceedings of the fifth joint conference on lexical and computational semantics","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Spitzer et al., 1967] Robert L Spitzer, Jacob Cohen, Joseph L Fleiss, and Jean Endicott. Quantification of agreement in psychiatric diagnosis: A new approach. Archives of General Psychiatry, 17(1):83-87, 1967.","title":"Quantification of agreement in psychiatric diagnosis: A new approach","authors":"L Robert, Jacob Spitzer, Joseph L Cohen, Jean Fleiss, Endicott","year":"1967","venue":"Archives of General Psychiatry","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tyagi et al., 2020a] Aman Tyagi, Matthew Babcock, Kath- leen M Carley, and Douglas C Sicker. Polarizing tweets on climate change. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pages 107-117. Springer, 2020.","title":"Polarizing tweets on climate change","authors":"Tyagi","year":"2020","venue":"International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Tyagi et al., 2020b] Aman Tyagi, Joshua Uyheng, and Kathleen M Carley. Affective polarization in online cli- mate change discourse on twitter. In 2020 IEEE/ACM In- ternational Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 443-447. IEEE, 2020.","title":"Affective polarization in online climate change discourse on twitter","authors":"Tyagi","year":"2020","venue":"IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Upadhyaya and Chandra, 2022] Apoorva Upadhyaya and Joydeep Chandra. Spotting flares: The vital signs of the viral spread of tweets made during communal incidents. ACM Transactions on the Web, 16(4):1-28, 2022.","title":"Spotting flares: The vital signs of the viral spread of tweets made during communal incidents","authors":"Chandra ; Upadhyaya, Apoorva Upadhyaya, Joydeep Chandra","year":"2022","venue":"ACM Transactions on the Web","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Upadhyaya et al., 2022a] Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. A multi-task model for sentiment aided stance detection of climate change tweets. arXiv preprint arXiv:2211.03533, 2022.","title":"A multi-task model for sentiment aided stance detection of climate change tweets","authors":"Upadhyaya","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Upadhyaya et al., 2022b] Apoorva Upadhyaya, Catharina Pfeiffer, Oleh Astappiev, Ivana Marenzi, Stefanie Lenzer, Andreas Nehring, and Marco Fisichella. How learnweb can support science education research on climate change in social media. In 12th International Conference on Methodologies and Intelligent Systems for Technology En- hanced Learning. Springer, 2022.","title":"How learnweb can support science education research on climate change in social media","authors":"Upadhyaya","year":"2022","venue":"12th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Upadhyaya et al., 2023a] Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. A multi-task model for emotion and offensive aided stance detection of climate change tweets. In Proceedings of the ACM Web Conference 2023, pages 3948-3958, 2023.","title":"A multi-task model for emotion and offensive aided stance detection of climate change tweets","authors":"Upadhyaya","year":"2023","venue":"Proceedings of the ACM Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Upadhyaya et al., 2023b] Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. Towards sentiment and temporal aided stance detection of climate change tweets. Information Processing & Management, 60(4):103325, 2023.","title":"Towards sentiment and temporal aided stance detection of climate change tweets","authors":"Upadhyaya","year":"2023","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Vaid et al., 2022] Roopal Vaid, Kartikey Pant, and Manish Shrivastava. Towards fine-grained classification of climate change related social media text. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 434-443, 2022.","title":"Towards fine-grained classification of climate change related social media text","authors":"Roopal Vaid, Kartikey Pant, Manish Shrivastava","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing sys- tems, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Vychegzhanin and Kotelnikov, 2021] Sergey Vy- chegzhanin and Evgeny Kotelnikov. A new method for stance detection based on feature selection tech- niques and ensembles of classifiers. IEEE Access, 9:134899-134915, 2021.","title":"A new method for stance detection based on feature selection techniques and ensembles of classifiers","authors":"Sergey Vychegzhanin, Evgeny Kotelnikov","year":"2021","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang and Wang, 2021] Limin Wang and Dexin Wang. Solving stance detection on tweets as multi-domain and multi-task text classification. IEEE Access, 9:157780- 157789, 2021.","title":"Solving stance detection on tweets as multi-domain and multi-task text classification","authors":"Limin Wang, Dexin Wang","year":"2021","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2020] Zhongqing Wang, Qingying Sun, Shoushan Li, Qiaoming Zhu, and Guodong Zhou. Neural stance detection with hierarchical linguistic representa- tions. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:635-645, 2020. [Wikipedia, 2001] Wikipedia. Convolution. https://en. wikipedia.org/wiki/Convolution, 2001. Accessed: 2023- 02-02.","title":"Neural stance detection with hierarchical linguistic representations","authors":"Wang","year":"2020","venue":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","doi":null,"url":"https://en.wikipedia.org/wiki/Convolution,2001","internal_url":null},{"position":35,"raw":"[Wu et al., 2021] Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, and Qinghua Peng. Improv- ing the accuracy in classification of blood pressure from photoplethysmography using continuous wavelet trans- form and deep learning. International journal of hyper- tension, 2021, 2021.","title":"Improving the accuracy in classification of blood pressure from photoplethysmography using continuous wavelet transform and deep learning","authors":"Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng","year":"2021","venue":"International journal of hypertension","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhou and Shen, 2021] Yanmengqian Zhou and Lijiang Shen. Confirmation bias and the persistence of misin- formation on climate change. Communication Research, page 00936502211028049, 2021.","title":"Confirmation bias and the persistence of misinformation on climate change","authors":"Shen Zhou, Yanmengqian Zhou, Lijiang Shen","year":"2021","venue":"Communication Research","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zirikly et al., 2019] Ayah Zirikly, Philip Resnik, Ozlem Uzuner, and Kristy Hollingshead. Clpsych 2019 shared task: Predicting the degree of suicide risk in reddit posts. In Proceedings of the sixth workshop on computational linguistics and clinical psychology, pages 24-33, 2019.","title":"Clpsych 2019 shared task: Predicting the degree of suicide risk in reddit posts","authors":"Ayah Zirikly, Philip Resnik, Ozlem Uzuner, Kristy Hollingshead","year":"2019","venue":"Proceedings of the sixth workshop on computational linguistics and clinical psychology","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}