Structural Models of Human Social Interactions in Online Smart Communities: the Case of Region-based Journalists on Twitter
Year: 2021
Type: preprint
Abstract: Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, whereby individuals cannot maintain meaningful interactions with more than 150 people, on average. In this work, we focus on the ego networks of journalists on Twitter, considering 17 different countries, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users, regardless of their country. Their ego network structure is very aligned with reference models derived from the social brain hypothesis and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter than with other social interaction means. From a dynamic perspective, journalists have stable short-term relationships that don’t change a lot over time. In the longer term, though, ego networks can be pretty dynamic, especially in the innermost circles. Moreover, the ego-alter ties of journalists are often information-driven, as they are mediated by hashtags. Finally, we observe that highly popular journalists tend to engage with other journalists of similar popularity, and vice versa. Instead, when interacting with generic users, their popularity doesn't seem to play a major role.
Source: arXiv (Cornell University)
Institution: National Research Council
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3201778564