Abstract
With such a great ease of sharing thoughts with the whole world and ever-increasing delightful features, social media such as Twitter has been influencing many aspects of our lives such as product recommendations, movie reviews, political campaigns, game predictions, and so on. It can be found that often few individuals tend to be influential enough to drive a whole group or network towards a particular thought. Network Centrality is one of the most widely studied and used concepts in social network analysis. There are other Twitter-specific measures known as influence metrics which are often used for social network analysis. The work presented in this paper reports on the implementation of some centrality measures and Twitter-specific influence measures are applied on a Twitter network of professional wrestling. The paper also reports on the correlation among the different measures for detecting influential users.
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References
Newman, M.E.J.: Networks: an introduction. Oxford University Press, Oxford (2010)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. WSDM 45–54 (2011)
Riquelme, F., Gonzlez-Cantergiani, P.: Measuring user influence on Twitter: a survey. Inf. Process. Manag. 52(5), 949–975 (2016)
Sun, J., Tang, J.: A survey of models and algorithms for social influence analysis. Social Network Data Analytics, pp. 177–214. Springer, Berlin (2011)
Noro, T., Ru, F., Xiao, F., Tokuda, T.: Twitter user rank using keyword search. Information Modelling and Knowledge Bases XXIV. Frontiers in Artificial Intelligence and Applications, vol. 251, pp. 31–40. IOS press, Amsterdam (2013)
Lee, C., Kwak, H., Park, H., Moon, S.B.: Finding influentials based on the temporal order of information adoption in Twitter. In: WWW, pp. 1137–1138 (2010)
Jabeur, L.B., Tamine, L., Boughanem, M.: Active microbloggers: identifying influencers, leaders and discussers in microblogging networks. In: SPIRE, pp. 111–117 (2012)
Romero, D.M., Galuba, W., Asur, S., Huberman, B.A.: Influence and passivity in social media. In: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD, vol. 6913, pp. 18–33 (2011)
Zhang, B., Zhong, S., Wen, K., Li, R., Gu, X.: Finding high-influence microblog users with an improved PSO algorithm. IJMIC 18(4), 349–356 (2013)
Nagmoti, R., Teredesai, A., De Cock, M.: Ranking approaches for microblog search. In: International Conference on Web Intelligence, WI 2010, pp. 153–157 (2010)
Aleahmad, A., Karisani, P., Rahgozar, M., Oroumchian, F.: OLFinder: finding opinion leaders in online social networks. J. Inf. Sci. 1–16 (2015)
Gayo-Avello, D.: Nepotistic relationships in Twitter and their impact on rank prestige algorithms. Inf. Process. Manag. 49(6), 1250–1280 (2013)
Khrabrov, A., Cybenko, G.: Discovering influence in communication networks using dynamic graph analysis. In: PASSAT, pp. 288–294 (2010)
Srinivasan, M.S., Srinivasa, S., Thulasidasan, S.: Exploring celebrity dynamics on Twitter. In: Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop, I-CARE 2013, pp. 1–4 (2013)
Hajian, B., White, T.: Modelling influence in a social network: metrics and evaluation. In: PASSAT/SocialCom, pp. 497–500 (2011)
Jin, X., Wang, Y.: Research on social network structure and public opinions dissemination of micro-blog based on complex network analysis. J. Netw. 8(7), 1543–1550 (2013)
Hirsch, J.E.: An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics 85(3), 741–754 (2010)
Anger, I., Kittl, C.: Measuring influence on Twitter. In: Lindstaedt, S.N., Granitzer, M. (eds.) I-KNOW 2011, 11th International Conference on Knowledge Management and Knowledge Technologies
Morone, F., Makse, H.A.: Influence maximization in complex networks through optimal percolation. Nature 524, 65–68 (2015)
Cha, M., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In: Association for the Advancement of Artificial Intelligence, pp. 10–17 (2010)
Cossu, J.-V., Dugu, N., Labatut, V.: Detecting real-world influence through Twitter. In: Second European Network Intelligence Conference, ENIC, vol. 2015, pp. 83–90 (2015)
Probst, F., Grosswiele, L., Pfleger, R.: Who will lead and who will follow: identifying influential users in online social networks - a critical review and future research directions. Bus. Inf. Syst. Eng. 5(3), 179–193 (2013)
Vogiatzis, D.: Influential users in social networks. Semantic Hyper/Multimedia Adaptation - Schemes and Applications. Studies in Computational Intelligence, vol. 418, pp. 271–295. Springer, Berlin (2013)
Nargundkar, A., Rao, Y.S.: InfluenceRank: a machine learning approach to measure influence of Twitter users. IEEE Explore, pp. 1–6 (2016). https://doi.org/10.1109/ICRTIT.2016.7569535
Batool, K., Niazi, M.A.: Towards a methodology for validation of centrality measures in complex networks (2014). https://doi.org/10.1371/journal.pone.0090283
Kardara, M., Papadakis, G., Papaoikonomou, A., Tserpes, K., Varvarigou, T.A.: Large-scale evaluation framework for local influence theories in Twitter. Inf. Process. Manag. 51(1), 226–252 (2015)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978) (Elsevier)
Bavelas, A.: A mathematical model for small group structures. Hum. Organ. 7, 16–30 (1948)
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Chakma, K., Chakraborty, R., Singh, S.K. (2020). Finding Correlation Between Twitter Influence Metrics and Centrality Measures for Detection of Influential Users. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_5
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