Abstract
Extremist YouTube videos are always associated with the ‘Dark side’ and potentially bring negative influence on younger viewers. These videos are deemed inappropriate for containing violent, has since triggered interest of academia and other stakeholders to study on its content. Extensive body of research mostly emphasizes on extremists’ messages, modus operandi, production features and may overlook on emotion perspective of the viewers. Viewing the videos can evoke specific emotional response in the viewers. Emotional response can be described in specific sets of adjective words or sentences which can be referred as emotional descriptors. This paper will report on factor analysis of 62 emotional descriptors rated by 80 university students after having watched 20 extremist YouTube videos during a Kansei evaluation. Significant emotional descriptors are successfully ascertained wherein 27 descriptors are retained in 3 factors; offensive, intrigue and awkward. The remaining 35 descriptors can be ignored as the proportions of variability explained by the remaining factors are close to zero and can be considered insignificant. The finding for ‘offensive’ and ‘awkward’ is expected since majority of the descriptors are of negative affect in nature. Rather interesting is the finding about ‘intrigue’ as a factor since it contains positive affect in circumstance which commonly perceived as negative. That however, adds to novelty for this research. Together the result shall benefit academia and other stakeholders in understanding evoked emotions in extremist YouTube videos and future work on affective video classification.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ekman, M.: The dark side of online activism: Swedish right-wing extremist video activism on YouTube. MedieKultur: Journal of media and communication research, 30(56), 79-99 (2014).
Winkler, C. K., Dauber, C. E.: Visual propaganda and extremism in the online environment. Strategic Studies Institute and US Army War College Press, Carlisle, PA (2014).
Koronaiou, A., Lagos, E., Sakellariou, A., Kymionis, S., Chiotaki-Poulou, I.: Golden Dawn, austerity and young people: The rise of fascist extremism among young people in contemporary Greek society. The Sociological Review, 63(2_suppl), 231-249 (2015).
Chen, H.: Dark web: Exploring and data mining the dark side of the web. Springer Science & Business Media, London (2011).
Sabbah, T., Selamat, A.: Hybridized Feature Set for Accurate Arabic Dark Web Pages Classification. In: International Conference on Intelligent Software Methodologies, Tools, and Techniques, pp. 175-189. Springer International Publishing (2015).
Salem, A., Reid, E., Chen, H.: Multimedia content coding and analysis: Unraveling the content of Jihadi extremist groups’ videos. Studies in Conflict & Terrorism 31(7), 605-626 (2008).
Rieger, D., Frischlich, L., Bente, G.: Propaganda 2.0: Psychological effects of right-wing and Islamic extremist internet videos. Luchterhand (2013).
Berger, J., Milkman, K.: Social transmission, emotion, and the virality of online content Wharton research paper 106 (2010).
Lokman, A. M., Nagamachi, M.: Kansei engineering: A beginner’s perspective. Upena (2010).
Rosli, R. M., Lokman, A. M.: Analysis of emotional descriptors for video-watching experience through Kansei evaluation. Advanced Science Letters 23(5), 4344-4348 (2017).
Guadagno, R. E., Rempala, D. M., Murphy, S., Okdie, B. M.: What makes a video go viral? An analysis of emotional contagion and Internet memes. Computers in Human Behavior 29(6), 2312-2319 (2013).
Wang, S., Ji, Q.: Video affective content analysis: a survey of state-of-the-art methods. IEEE Transactions on Affective Computing 6(4), 410-430 (2015).
Yan, H. B., Huynh, V. N., Nakamori, Y.: A group nonadditive multiattribute consumer-oriented Kansei evaluation model with an application to traditional crafts. Annals of Operations Research 195(1), 325-354 (2012).
Matsubara, T., Matsubara, Y., Ishihara, S., Nagamachi, M.: PLS-based approach for Kansei analysis. In Proceedings: Fifth International Workshop on Computational Intelligence & Applications, vol. 2009, pp. 94-98. IEEE SMC Hiroshima Chapter (2009).
Luo, S. J., Fu, Y. T., Korvenmaa, P.: A preliminary study of perceptual matching for the evaluation of beverage bottle design. International Journal of Industrial Ergonomics 42(2), 219-232 (2012).
Lokman, A. M.: KE as affective design methodology. In Computer, Control, Informatics and Its Applications (IC3INA), pp. 7-13. IEEE, (2013).
Yong, A. G., Pearce, S.: A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in quantitative methods for psychology 9(2), 79-94 (2013).
Tavakol, M., Dennick, R.: Making sense of Cronbach’s alpha. International journal of medical education 2, 53-55 (2011).
Jindo, T., & Hirasago, K.: Application studies to car interior of Kansei engineering. International journal of industrial ergonomics 19(2), 105-114 (1997).
Yang, C. C.: A classification-based Kansei engineering system for modeling consumers’ affective responses and analyzing product form features. Expert Systems with Applications 38(9), 11382-11393 (2011).
Adnan, H., Redzuan, F.: Evaluating students’ emotional response in video-based learning using Kansei Engineering. In User Science and Engineering (i-USEr), 2016 4th International Conference, pp. 237-242. IEEE, (2016).
Kaiser, H. F.: The varimax criterion for analytic rotation in factor analysis. Psychometrika 23(3), 187-200 (1958).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rosli, R.M., Lokman, A.M., Aris, S.R.S. (2018). Analysis of Evoked Emotions in Extremist YouTube Videos Through Kansei Evaluation. In: Lokman, A., Yamanaka, T., Lévy, P., Chen, K., Koyama, S. (eds) Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018. KEER 2018. Advances in Intelligent Systems and Computing, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-10-8612-0_77
Download citation
DOI: https://doi.org/10.1007/978-981-10-8612-0_77
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8611-3
Online ISBN: 978-981-10-8612-0
eBook Packages: EngineeringEngineering (R0)