Understanding how information flows in social networks is critical to counteracting dangerous misinformation, promoting the spreading of news, and designing healthy online social environments. Scholars have long realized the role of information superspreaders—namely, users with the capability to spread messages and ideas to many others rapidly.
Understanding how information flows in social networks is critical to counteracting dangerous misinformation, promoting the spreading of news, and designing healthy online social environments. Scholars have long realized the role of information superspreaders—namely, users with the capability to spread messages and ideas to many others rapidly.
A long-standing research tradition identifies the superspreaders through their position in the social network. Recent research, published in the journal National Science Review and led by Prof. Linyuan Lü (University of Electronic Science and Technology of China) and Dr. Manuel S. Mariani (University of Zurich), challenges this long-standing paradigm. It shows that users‘ behavioral traits (i.e., how they tend to behave) provide more accurate early indicators of their spreading ability than where they sit in the social network.
The authors departed from traditional network approaches by starting with a model for how information flows from individual to individual. Motivated by previous empirical findings, the model assumes that the probability that a message is transmitted from a source to a target user is determined by both the source’s influence (namely, a parameter that captures her likelihood to transmit information to others) and the target’s susceptibility to influence.
