TY - GEN
T1 - Trends of news diffusion in social media based on crowd phenomena
AU - Kim, Minkyoung
AU - Newth, David
AU - Christen, Peter
N1 - Publisher Copyright:
© Copyright 2014 by the International World Wide Web Conferences Steering Committee.
PY - 2014/4/7
Y1 - 2014/4/7
N2 - Information spreads across social media, bringing heterogeneous social networks interconnected and diffusion patterns varied in different topics of information. Studying such cross-population diffusion in various context helps us understand trends of information diffusion in a more accurate and consistent way. In this study, we focus on realworld news diffusion across online social systems such as mainstream news (News), social networking sites (SNS), and blogs (Blog), and we analyze behavioral patterns of the systems in terms of activity, reactivity, and heterogeneity. We found that News is the most active, SNS is the most reactive, and Blog is the most persistent, which governs time-evolving heterogeneity of these systems. Finally, we interpret the discovered crowd phenomena from various angles using our previous model-free and model-driven approaches, showing that the strength and directionality of influence reect the behavioral patterns of the systems in news diffusion.
AB - Information spreads across social media, bringing heterogeneous social networks interconnected and diffusion patterns varied in different topics of information. Studying such cross-population diffusion in various context helps us understand trends of information diffusion in a more accurate and consistent way. In this study, we focus on realworld news diffusion across online social systems such as mainstream news (News), social networking sites (SNS), and blogs (Blog), and we analyze behavioral patterns of the systems in terms of activity, reactivity, and heterogeneity. We found that News is the most active, SNS is the most reactive, and Blog is the most persistent, which governs time-evolving heterogeneity of these systems. Finally, we interpret the discovered crowd phenomena from various angles using our previous model-free and model-driven approaches, showing that the strength and directionality of influence reect the behavioral patterns of the systems in news diffusion.
KW - Crowd phenomena
KW - News diffusion
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=84990975406&partnerID=8YFLogxK
U2 - 10.1145/2567948.2579325
DO - 10.1145/2567948.2579325
M3 - Conference contribution
T3 - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
SP - 753
EP - 758
BT - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
T2 - 23rd International Conference on World Wide Web, WWW 2014
Y2 - 7 April 2014 through 11 April 2014
ER -