An Empirical Study of How Users Adopt Famous Entities

TL;DR

This study analyzes how users follow famous entities on social networks, revealing unique distribution patterns and providing insights that could enhance marketing strategies and user classification.

Contribution

It presents the first empirical analysis of human-selected famous entities in social networks, uncovering their unique follow distribution patterns.

Findings

Famous entities' in-degree does not follow a power-law distribution.

Maximum followees per user in a category exhibit power-law behavior.

Insights may benefit microblogging marketing and user classification.

Abstract

Users of social networking services construct their personal social networks by creating asymmetric and symmetric social links. Users usually follow friends and selected famous entities that include celebrities and news agencies. In this paper, we investigate how users follow famous entities. We statically and dynamically analyze data within a huge social networking service with a manually classified set of famous entities. The results show that the in-degree of famous entities does not fit to power-law distribution. Conversely, the maximum number of famous followees in one category for each user shows power-law property. To our best knowledge, there is no research work on this topic with human-chosen famous entity dataset in real life. These findings might be helpful in microblogging marketing and user classification.