EVALUATING THE SOCIAL IMPACT OF INTERNET MEDIA NEWS

• Authors: Zakharchenko Artem
• Languages of publication: EN

Abstracts

EN

The goal of the research is to develop a method for measuring topical social information impact on active people through monitoring the dynamics of social networks users interaction. We introduced the concept of interactive potential which can be determined through dynamics curve analysis in order to interact with information. Regular measuring of news’ interactive potential allows tracing the dynamics of social interest in some topics. We used the method to analyze trending topics in Ukrainian media and to describe the dynamics of people’s concern with political life and their readiness for public protests.

Keywords

EN

Interactive potential,; information spreading,; information impact,; social networks,; online media

• Publisher: Uniwersytet Warmińsko-Mazurski w Olsztynie
• Journal: Civitas et Lex
• Year: 2017
• Issue: 3(15)
• Pages: 7-21

Dates

published

2017-09-30

Contributors

author

Zakharchenko Artem

• Taras Shevchenko National University of Kyiv

References

• 1. A.F. da Rocha, E. Massad, P.C.C. dos Santos, A. Pereira, A neurobiologically inspired model of social cognition: Memes spreading in the Internet, „Biol. Inspired Cogn. Archit”, V. 141(2015), p. 86–96.
• 2. E. Even-Dar, A. Shapira, A note on maximizing the spread of influence in social networks, „Inf. Process. Lett”, V. 111, Is. 4(2011), pp.184–187.
• 3. A. Singh, Y.N. Singh, Rumor dynamics in weighted scale-free networks with degree correlations, „J. Complex Networks”, V. 3, Is. 3(2015), pp. 450–468. doi:10.1093/comnet/cnu047.
• 4. W. Galuba, K. Aberer, Outtweeting the Twitterers − Predicting Information Cascades in Microblogs, “Proceedings of the 3rd Conference on Online Social Networks”, USENIX Association Berkeley (2010), pp. 3−11.
• 5. M. Nekovee, Y. Moreno, G. Bianconi, M. Marsili, Theory of rumour spreading in complex social networks, „Physica A: Statistical Mechanics and its Applications”, vol. 374, no. 1(2007), pp 457–470.
• 6. T. Kawamoto, N. Hatano, Viral spreading of daily information in online social networks, „Physica A: Statistical Mechanics and its Applications”, 406(2014), pp 34–41.
• 7. A. Guille, H. Hacid, A predictive model for the temporal dynamics of information diffusion in online social networks, WWW ‘12 Companion Proceedings of the 21st International Conference on World Wide Web (2012), pp. 1145−1152.
• 8. G.G. Pocheptsov, «House of Cards»: how the clip thinking changes to series thinking, “Khvylia”, (2016), 11 March, available at: <http://hvylya.net/analytics/society/kartochnyiy-domik-kak-na-smenu-klipovomu-myishleniyu-prihodit-serialnoe.html>, accessed June 12, 2017.
• 9. А. Zakharchenko, Measurement of the efficiency of message impact on the activity of social networks members, „Aktualjni pytannja masovoji komunikaciji”, Is. 15(2014), pp 36−49.