Aishwarya N. Reganti
ABSTRACT
The Islamic State of Iraq and Syria (ISIS) is a Salafi jihadist militant group that has made extensive use of online social media platforms to promulgate its ideologies and evoke many individuals to support the organization. The psycho-sociological background of an individual plays a crucial role in determining his/her vulnerability of being lured into joining the organisation and indulge in terrorist activities, since his/her behavior largely depends on the society s/he was brought up in. Here, we analyse five sociological aspects -- personality, values & ethics, optimism/pessimism, age and gender to understand the psycho-sociological vulnerability of individuals over Twitter. Experimental results suggest that psycho-sociological aspects indeed act as foundation to discover and differentiate between prominent and unobtrusive users in Twitter.
- L. Curtis, L. Coffey, D. Inserra, D. Kochis, W. Lohman, J. Meservey, J. Phillips, and R. Simcox, "Combatting the isis foreign fighter pipeline: A global approach," The Heritage Foundation Special Report on Terrorism, 2016.Google Scholar
- J. Berger and J. Morgan, "The isis twitter census: Defining and describing the population of isis supporters on twitter," The Brookings Project on US Relations with the Islamic World, vol. 3, no. 20, 2015.Google Scholar
- W. M. Walid, "# failedrevolutions: Using twitter to study the antecedents of isis support," arXiv preprint arXiv, vol. 1503, 2005.Google Scholar
- A. Bermingham, M. Conway, L. McInerney, N. O'Hare, and A. F. Smeaton, "Combining social network analysis and sentiment analysis to explore the potential for online radicalisation," in ASONAM. IEEE, 2009, pp. 231--236.Google Scholar
- L. Blaker, "The islamic states use of online social media," Military Cyber Affairs, vol. 1, no. 1, p. 4, 2015. Google ScholarCross Ref
- H. Saif, M. Fernández, M. Rowe, and H. Alani, "On the role of semantics for detecting pro-isis stances on social media," in ISWC 2016, Kobe, Japan, October, 2016., 2016.Google Scholar
- T. Maheshwari, A. N. Reganti, T. Chakraborty, and A. Das, "Socioethnic ingredients of social network communities," in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, Portland, OR, USA, February 25 - March 1, 2017, Companion Volume, 2017, pp. 235--238. [Online]. Available: http://dl.acm.org/citation.cfm?id=3026322Google Scholar
- L. R. Goldberg, "An alternative "description of personality": the big-five factor structure," Journal of personality and social psychology, vol. 59, no. 6, p. 1216, 1990. Google ScholarCross Ref
- S. H. Schwartz, "Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries," in Advances in Experimental Social Psychology, M. Zanna, Ed. New York: Academic Press, 1992, vol. 25, pp. 1--65.Google Scholar
- X. Ruan, S. R. Wilson, and R. Mihalcea, "Finding optimists and pessimists on twitter," in The 54th Annual Meeting of the Association for Computational Linguistics, 2016, p. 320. Google ScholarCross Ref
- F. Rangel, P. Rosso, B. Verhoeven, W. Daelemans, M. Potthast, and B. Stein, "Overview of the 4th author profiling task at pan 2016: cross-genre evaluations," Working Notes Papers of the CLEF, 2016.Google Scholar
- S. M. Mohammad, S. Kiritchenko, and X. Zhu, "NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets," arXiv preprint arXiv:1308.6242, 2013.Google Scholar
- A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC, vol. 6. Citeseer, 2006, pp. 417--422.Google Scholar
- F. Celli, F. Pianesi, D. Stillwell, and M. Kosinski, "The workshop on computational personality recognition 2013," in Proceedings of the AAAI. AAAI, 2013, pp. 2--5.Google Scholar
- B. Verhoeven, W. Daelemans, and T. De Smedt, "Ensemble methods for personality recognition," in Proceedings of WCPR13, in conjunction with ICWSM-13, 2013.Google Scholar
- S. H. Schwartz, G. Melech, A. Lehmann, S. Burgess, M. Harris, and V. Owens, "Extending the cross-cultural validity of the theory of basic human values with a different method of measurement," Journal of cross-cultural psychology, vol. 32, no. 5, pp. 519--542, 2001. Google ScholarCross Ref
- T. Maheshwari, A. N. Reganti, U. Kumar, T. Chakraborty, and A. Das, "Semantic interpretation of social network communities," in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4--9, 2017, San Francisco, California, USA., 2017, pp. 4967--4968.Google Scholar
- P. R. Monge and N. S. Contractor, Theories of communication networks. Oxford University Press, USA, 2003.Google Scholar
- S. Cutter, B. J. Boruff, and W. L. Shirley, "Social vulnerability to environmental hazards," Hazards, Vulnerability, and Environmental Justice, pp. 115--132, 2006.Google Scholar
- Understanding Psycho-Sociological Vulnerability of ISIS Patronizers in Twitter
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