Measuring and analyzing charisma on twitter using combination weighting and TOPSIS method

Document Type : Research Paper


Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran


In this research, charisma has been measured at different levels of online social networks including charisma at the level of messages, individuals, and communities. First, the charisma-associated features have been extracted and then weighted by hybrid proposed methods. Eventually, measuring and ranking charisma has been investigated through technique for order preference by similarity to ideal situation (TOPSIS) as one of the leading multi-criteria decision-making methods. Through the proposed approach, the charisma of different messages, individuals, as well as implicit and explicit communities can be measured, ranked, and compared. In this research, eight datasets were collected from Twitter with different and diverse features. The results indicated that the charismatic messages and individuals of each dataset have been chosen properly and logically. Further, a method has been presented to measure the rate of charisma in every community which can be employed for comparing communities and predicting behavior in online communities.


[1] A. Aleahmad, et al., OLFinder: Finding opinion leaders in online social networks, Journal of Information Science,
42(5) (2016) 659-674.
[2] Z.Z. Alp, and S¸.G. O˘g¨ud¨uc¨u, ¨ Identifying topical influencers on twitter based on user behavior and network topology,
Knowledge-Based Systems, 141 (2018) 211-221.
[3] B.C. Arnold, Pareto and generalized Pareto distributions, in Modeling income distributions and Lorenz curves,
(2008) 119-145.
[4] S¸. Aslan, The relation of charismatic leadership and organizational citizenship behavior: The role
of ’tenure’and’salary’variables, Journal of Human Sciences, 6(1) (2009) 256-275.
[5] B.J. Avolio, B.M. Bass, and D.I. Jung, Re-examining the components of transformational and transactional
leadership using the Multifactor Leadership, Journal of occupational and organizational psychology, 72(4) (1999)
[6] A.L. Barab´asi, and E. Bonabeau, Scale-free networks, Scientific american, 288(5) (2003) 60-69.
[7] J.M. Beyer, Taming and promoting charisma to change organizations, The Leadership Quarterly, 10(2) (1999)
[8] M. Bouguessa, and L.B. Romdhane, Identifying authorities in online communities, ACM Transactions on Intelligent Systems and Technology (TIST), 6(3) (2015) 30.
[9] M. Cha,, et al. Measuring user influence in twitter: The million follower fallacy. in fourth international AAAI
conference on weblogs and social media. 2010.
[10] A. Clauset, C.R. Shalizi, and M.E. Newman, Power-law distributions in empirical data, SIAM review, 51(4)
(2009) 661-703.
[11] T. Cleff, Exploratory data analysis in business and economics, 2014.
[12] J.A. Conger, and R.N. Kanungo, Charismatic leadership in organizations: Perceived behavioral attributes and
their measurement, Journal of organizational behavior, 15(5) (1994) 439-452.
[13] M. De Domenico, et al., The anatomy of a scientific rumor, Scientific reports, 3 (2013) 2980.
[14] N. Delgarm, B. Sajadi, and S. Delgarm, Multi-objective optimization of building energy performance and indoor
thermal comfort: A new method using artificial bee colony (ABC), Energy and Buildings, 131 (2016) 42-53.
[15] E.C. Demircio˘glu, Evaluation of Charismatic Leadership from Management Perspective, Uluslararası Akademik
Y¨onetim Bilimleri Dergisi, 1(1) (2015) 52-69.[16] Y. Du, , et al., A new method of identifying influential nodes in complex networks based on TOPSIS, Physica A:
Statistical Mechanics and its Applications, 399 (2014) 57-69.
[17] H. Fani, et al., User community detection via embedding of social network structure and temporal content, Information Processing & Management, (2019) p. 102056.
[18] J.B. Fuller, et al., A quantitative review of research on charismatic leadership, Psychological reports, 78(1) (1996)
[19] D. Gayo-Avello, Nepotistic relationships in twitter and their impact on rank prestige algorithms, Information
Processing & Management, 49(6) (2013) 1250-1280.
[20] A. Grabo, B.R. Spisak, and M. van Vugt, Charisma as signal: An evolutionary perspective on charismatic
leadership, The Leadership Quarterly, 28(4) (2017) 473-485.
[21] D. Gul, and A.S. Uludag, Determination of the Most Charismatic Leader Using Analytic Hierarchy Process and
Fuzzy TOPSIS: An Application in Turkey, International Business Research, 9(7) (2016) 80-97.
[22] J. Henrich, M. Chudek, and R. Boyd, The Big Man Mechanism: how prestige fosters cooperation and creates prosocial leaders, Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1683) ( 2015)
[23] J. Herzig, Y. Mass, and H. Roitman. An author-reader influence model for detecting topic-based influencers in
social media, in Proceedings of the 25th ACM conference on Hypertext and social media. 2014.
[24] R.J. House, W.D. Spangler, and J. Woycke, Personality and charisma in the US presidency: A psychological
theory of leadership effectiveness, in Academy of Management Proceedings, Academy of Management Briarcliff
Manor, NY 10510, 1990.
[25] J. Hu, , et al., A modified weighted TOPSIS to identify influential nodes in complex networks, Physica A: Statistical
Mechanics and its Applications, 444 (2016) 73-85.
[26] C.L. Hwang, and K. Yoon, Methods for multiple attribute decision making, in Multiple attribute decision making,
(1981) 58-191.
[27] L. Jain,and R. Katarya, Discover opinion leader in online social network using firefly algorithm, Expert Systems
with Applications, 122 (2019) 1-15.
[28] P.P. Kalbar, S. Karmakar, and S.R. Asolekar, Selection of an appropriate wastewater treatment technology: A
scenario-based multiple-attribute decision-making approach, Journal of environmental management, 113 (2012)
[29] G. Katsimpras, D. Vogiatzis, and G. Paliouras, Determining influential users with supervised random walks, in
Proceedings of the 24th International Conference on World Wide Web. 2015.
[30] K. Kim, F. Dansereau, and I.S. Kim, Extending the concept of charismatic leadership: An illustration using
Bass’s (1990) categories, in Transformational and Charismatic Leadership: The Road Ahead 10th Anniversary
Edition, Emerald Group Publishing Limited, (2013) 165-194.
[31] W.R. Knight, A computer method for calculating Kendall’s tau with ungrouped data, Journal of the American
Statistical Association, 61(314) (1966) 436-439.
[32] J. Leskovec, and J.J. Mcauley, Learning to discover social circles in ego networks, in Advances in neural information processing systems. 2012.
[33] B. MacLachlan, The age of grace: Charis in early Greek poetry, 2014.
[34] A. Mardani, et al., Multiple criteria decision-making techniques and their applications–a review of the literature
from 2000 to 2014, Economic Research-Ekonomska Istraˇzivanja, 28(1) (2015) 516-571.
[35] R. Nagmoti, A. Teredesai, and M. De Cock, Ranking approaches for microblog search, in Proceedings of the 2010
IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01.
[36] O. Niebuhr, J. Voße, and A. Brem, What makes a charismatic speaker? A computer-based acoustic-prosodic
analysis of Steve Jobs tone of voice, Computers in Human Behavior, 64 (2016) 366-382.
[37] A. Pal, and S. Counts, Identifying topical authorities in microblogs, in Proceedings of the fourth ACM international
conference on Web search and data mining. 2011.
[38] C.S. Park, and B.K. Kaye, The tweet goes on: Interconnection of Twitter opinion leadership, network size, and
civic engagement, Computers in Human Behavior, 69 (2017) 174-180.
[39] F. Riquelme, and P. Gonz´alez-Cantergiani, Measuring user influence on Twitter: A survey, Information Processing & Management, 52(5)(2016) 949-975.
[40] C.E. Shannon, A mathematical theory of communication, Bell system technical journal, 27(3) (1948) 379-423.
[41] A. Silva,, et al. ProfileRank: finding relevant content and influential users based on information diffusion, in
Proceedings of the 7th Workshop on Social Network Mining and Analysis. 2013.
[42] D. Simmie, M.G. Vigliotti, and C. Hankin, Ranking twitter influence by combining network centrality and influenceobservables in an evolutionary model, Journal of Complex Networks, 2(4) (2014) 495-517.
[43] A. Towler, , et al., How charismatic trainers inspire others to learn through positive affectivity, Computers in
Human Behavior, 32 (2014) 221-228.
[44] M. Weber, The theory of economic and social organization, Trans. AM Henderson and Talcott Parsons. New
York: Oxford University Press, 1947.
[45] A.D. Well, and J.L. Myers, Research design & statistical analysis. 2003.
[46] J. Yang, and J. Leskovec, Patterns of temporal variation in online media, in Proceedings of the fourth ACM
international conference on Web search and data mining. 2011.
[47] X. You, Y. Ma, and Z. Liu, A three-stage algorithm on community detection in social networks, Knowledge-Based
Systems, 2019.
[48] J. Yuan, et al., Topology-based algorithm for users’ influence on specific topics in micro-blog, JOURNAL OF
[49] R. Zafarani, M.A. Abbasi, and H. Liu, Social media mining: an introduction, 2014.
[50] A. Zareie, A. Sheikhahmadi, and K. Khamforoosh, Influence maximization in social networks based on TOPSIS,
Expert Systems with Applications, 108 (2018) 96-107.
[51] E.K. Zavadskas, et al., Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues, Economic research-Ekonomska istraˇzivanja, 29(1) (2016) 857-887.
[52] E.K. Zavadskas, Z. Turskis, and S. Kildien˙e, State of art surveys of overviews on MCDM/MADM methods,
Technological and economic development of economy, 20(1) (2014) 165-179. 
Volume 12, Special Issue
December 2021
Pages 1143-1158
  • Receive Date: 01 June 2021
  • Revise Date: 06 September 2021
  • Accept Date: 15 September 2021