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Affect in social media: The role of audience and the presence of contempt in cyberbullying

Published online by Cambridge University Press:  30 October 2017

Mihaela Cocea
Mihaela Cocea*
Affiliation:
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, United Kingdom. mihaela.cocea@port.ac.ukhttp://coceam.myweb.port.ac.uk/
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Abstract

Gervais & Fessler's Attitude–Scenario–Emotion (ASE) model is a useful tool for the detection of affect in social media. In this commentary, an addition to the model is proposed – the audience – and its role in the manifestation of affect is discussed using a cyberbullying scenario. The presence of contempt in cyberbullying is also discussed.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e233
Copyright
Copyright © Cambridge University Press 2017 

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References

Altrabsheh, N., Cocea, M. & Fallahkhair, S. (2015) Detecting sarcasm from students' feedback in Twitter. In: Design for teaching and learning in a networked world: Proceedings of the 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 15-18, 2015, ed. Conole, G., Klobučar, T., Rensing, C., Konert, J., & Lavoué, E., pp. 551–55. Springer.Google Scholar
Balahur, A. (2013) Sentiment analysis in social media texts. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, Georgia, 14 June, 2013, ed. Balahur, A., van der Goot, E., & Montoyo, A., pp. 120–28. Association for Computational Linguistics.Google Scholar
Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A. & De Bourdeaudhuij, I. (2014) Cyberbullying on social network sites: An experimental study into bystanders' behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior 31:259–71.Google Scholar
Bernstein, M. S., Bakshy, E., Burke, M. & Karrer, B. (2013) Quantifying the invisible audience in social networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, April 27-May 2, 2013, ed. Mackay, W. E., Brewster, S. A., & Bødker, S., pp. 21–30. ACM Digital Library/Association for Computing Machinery.Google Scholar
Bertolotti, T. & Magnani, L. (2013) A philosophical and evolutionary approach to cyber-bullying: Social networks and the disruption of sub-moralities. Ethics and Information Technology 15(4):285–99.Google Scholar
Dinakar, K., Jones, B., Havasi, C., Lieberman, H. & Picard, R. (2012) Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Transactions on Interactive Intelligent Systems (TIIS) 2(3): article 18:1–30.CrossRefGoogle Scholar
Ekman, P. (1992b) Are there basic emotions? Psychological Review 99(3):550–53.CrossRefGoogle ScholarPubMed
Jones, S. E., Manstead, A. S. R. & Livingstone, A. G. (2011) Ganging up or sticking together? Group processes and children's responses to text-message bullying. British Journal of Psychology 102:71–96.CrossRefGoogle ScholarPubMed
Justo, R., Corcoran, T., Lukin, S. M., Walker, M. & Torres, M. I. (2014) Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web. Knowledge-Based Systems 69:124–33.Google Scholar
Litt, E. (2012) Knock, knock. Who's there? The imagined audience. Journal of Broadcasting & Electronic Media 56(3):330–45.CrossRefGoogle Scholar
Liu, B. & Zhang, L. (2012) A survey of opinion mining and sentiment analysis. In: Mining text data, ed. Aggarwal, C. C. & Zhai, C., pp. 415–63. Springer.Google Scholar
Marwick, A. E. (2011) I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media and Society 13(1):114–33.CrossRefGoogle Scholar
Munezero, M., Montero, C. S., Kakkonen, T., Sutinen, E., Mozgovoy, M. & Klyuev, V. (2014a) Automatic detection of antisocial behaviour in texts. Informatica 38(1):3–10.Google Scholar
Munezero, M., Montero, C. S., Sutinen, E. & Pajunen, J. (2014b) Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Transactions on Affective Computing 5(2):101–11.CrossRefGoogle Scholar
Paltoglou, G. & Thelwall, M. (2012) Twitter, MySpace, Digg: Unsupervised sentiment analysis in social media. ACM Transactions on Intelligent Systems and Technology (TIST) 3(4): article 66:1–19.Google Scholar
Paltoglou, G. & Thelwall, M. (2013) Seeing stars of valence and arousal in blog posts. IEEE Transactions on Affective Computing 4(1):116–23.CrossRefGoogle Scholar
Reyes, A., Rosso, P. & Buscaldi, D. (2012) From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering 74:1–12.CrossRefGoogle Scholar
Russell, J. A. & Mehrabian, A. (1977) Evidence for a three-factor theory of emotions. Journal of Research in Personality 11(3):273–94.CrossRefGoogle Scholar
Tokunaga, R. S. (2010) Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior 26(3):277–87.CrossRefGoogle Scholar
Tromp, E. & Pechenizkiy, M. (2015) Pattern-based emotion classification on social media. In: Advances in social media analysis, ed. Gaber, M. M., Cocea, M., Wiratunga, N. & Goker, A., pp. 1–20. Springer.Google Scholar
Zhao, R., Zhou, A. & Mao, K. (2016) Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, vol. 43, pp. 1–6. ACM (Association for Computing Machinery).Google Scholar