Abstract
Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been fully explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from various topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. (Code and dataset are publicly available at https://github.com/ChenK7166/controversy-emotion). We find controversial comments express more anger and less admiration, joy, and optimism than non-controversial comments. Moreover, controversial comments affect emotions of downstream comments, resulting in a long-term increase in anger and a decrease in positive emotions. The magnitude and direction of emotional change differ by forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding the dynamics of emotions in online discussions can help communities to manage conversations better.
Keywords
- Controversy
- Emotion
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
Results do not differ qualitatively when using a higher threshold of the number of controversial comments to define controversial discussions.
References
Alhuzali, H., Ananiadou, S.: SpanEmo: casting multi-label emotion classification as span-prediction. In: European ACL, pp. 1573–1584 (2021)
Plaza-del Arco, F.M., Molina-González, M.D., Urena-López, L.A., Martín-Valdivia, M.T.: Comparing pre-trained language models for Spanish hate speech detection. Expert Syst. Appl. 166, 114120 (2021)
Bar-Tal, D., Halperin, E., De Rivera, J.: Collective emotions in conflict situations: Societal implications. J. Soc. Issues 63(2), 441–460 (2007)
Barbieri, F., Anke, L., Camacho-Collados, J.: XLM-t: multilingual language models in twitter for sentiment analysis and beyond (2021)
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., Blackburn, J.: The pushshift reddit dataset. In: ICWSM, vol. 14, pp. 830–839 (2020)
Bi, N.C.: How emotions and issue controversy influence the diffusion of societal issues with imagined audience on facebook. Beh. Inf. Technol. 41(6), 1245–1257 (2022)
Brady, W.J., McLoughlin, K., Doan, T.N., Crockett, M.J.: How social learning amplifies moral outrage expression in online social networks. Sci. Adv. 7(33), eabe5641 (2021)
Brady, W.J., Wills, J.A., Jost, J.T., Tucker, J.A., Van Bavel, J.J.: Emotion shapes the diffusion of moralized content in social networks. PNAS 114(28), 7313–7318 (2017)
Chochlakis, G., Mahajan, G., Baruah, S., Burghardt, K., Lerman, K., Narayanan, S.: Using emotion embeddings to transfer knowledge between emotions, languages, and annotation formats. In: ICASSP, pp. 1–5. IEEE (2023)
Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116 (2019)
Coviello, L., et al.: Detecting emotional contagion in massive social networks. PLoS ONE 9(3), e90315 (2014)
Demszky, D., et al.: Goemotions: a dataset of fine-grained emotions. arXiv preprint arXiv:2005.00547 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171–4186 (2019)
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: EMNLP, pp. 38–45. ACL (2020)
Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 1–27 (2018)
Haidt, J.: Why the past 10 years of American life have been uniquely stupid. The Atlantic (2022)
Hessel, J., Lee, L.: Something’s brewing! early prediction of controversy-causing posts from discussion features. In: ACL, pp. 1648–1659 (2019)
Koncar, P., Walk, S., Helic, D.: Analysis and prediction of multilingual controversy on reddit. In: WebScience. WebSci 2021, pp. 215–224 (2021)
Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach. arXiv: abs/1907.11692 (2019)
MacAvaney, S., Yao, H.R., Yang, E., Russell, K., Goharian, N., Frieder, O.: Hate speech detection: challenges and solutions. PLoS ONE 14(8), e0221152 (2019)
Mejova, Y., Zhang, A.X., Diakopoulos, N., Castillo, C.: Controversy and sentiment in online news. arXiv preprint arXiv:1409.8152 (2014)
Mohammad, S., Bravo-Marquez, F., Salameh, M., Kiritchenko, S.: Semeval-2018 task 1: Affect in tweets. In: SemEval, pp. 1–17 (2018)
Park, C.Y., et al.: Detecting community sensitive norm violations in online conversations. In: Findings of EMNLP, pp. 3386–3397 (2021)
Poletto, F., Basile, V., Sanguinetti, M., Bosco, C., Patti, V.: Resources and benchmark corpora for hate speech detection: a systematic review. Lang. Resour. Eval. 55(2), 477–523 (2021)
Stieglitz, S., Dang-Xuan, L.: Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29(4), 217–248 (2013)
Van Kleef, G.A., Cheshin, A., Fischer, A.H., Schneider, I.K.: The social nature of emotions. Front. Psychol. 7, 896 (2016)
Zayats, V., Ostendorf, M.: Conversation modeling on Reddit using a graph-structured LSTM. Trans. ACL 6, 121–132 (2018)
Acknowledgments
This material is based upon work supported in part by the Defense Advanced Research Projects Agency (DARPA) under Agreements No. HR00112290025 and HR001121C0168, and in part by the Air Force Office for Scientific Research (AFOSR) under contract FA9550-20-1-0224.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, K., He, Z., Chang, RC., May, J., Lerman, K. (2023). Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit. In: Thomson, R., Al-khateeb, S., Burger, A., Park, P., A. Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2023. Lecture Notes in Computer Science, vol 14161. Springer, Cham. https://doi.org/10.1007/978-3-031-43129-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-43129-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43128-9
Online ISBN: 978-3-031-43129-6
eBook Packages: Computer ScienceComputer Science (R0)