title: Competing for Attention in Social Media under Information Overload Conditions.
abstract: Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.
authors.affiliation: Department of Physics and Centre for Computational Science and Engineering, National University of Singapore, 117542, Singapore; Complex Systems Programme, Institute of High Performance Computing, Agency for Science Technology and Research, 138632, Singapore.
authors.forename: Ling
authors.initials: L
authors.lastname: Feng
authors.affiliation: School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China; School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Physics and Centre for Computational Science and Engineering, National University of Singapore, 117542, Singapore; Center for Phononics and Thermal Energy Science, School of Physics Science and Engineering, Tongji University, 200092, Shanghai, China.
authors.forename: Baowen
authors.initials: B
authors.lastname: Li
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, United States of America.
authors.forename: H Eugene
authors.initials: HE
authors.lastname: Stanley
authors.affiliation: Department of Physics, Bar-Ilan University, 52900 Ramat-Gan, Israel.
authors.forename: Shlomo
authors.initials: S
authors.lastname: Havlin
authors.affiliation: Instituto de Investigaciones Fisicas de Mar del Plata (IFIMAR), Universidad Nacional de Mar del Plata-CONICET, Funes 3350, (7600) Mar del Plata, Argentina.
authors.forename: Lidia A
authors.initials: LA
authors.lastname: Braunstein
mesh_terms: D001288:Attention; D003163:Competitive Behavior; D033062:Friends / Q000523:psychology; D006801:Humans; D033181:Information Dissemination; D020407:Internet; D008960:Models, Psychological; D011336:Probability; D012919:Social Behavior; D060756:Social Networking