Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T13:32:23.330Z Has data issue: false hasContentIssue false

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

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
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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. 2130. 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 18:130.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 102:7196.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):310.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 66:119.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 74:112.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. 120. 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. 16. ACM (Association for Computing Machinery).Google Scholar