Follower--Followee Ratio Category and User Vector for Analyzing Following Behavior

TL;DR

This paper presents a method to analyze user following behavior by categorizing users based on follower-followee ratios and user vectors derived from tweets and data, revealing different intentions behind following actions.

Contribution

It introduces a novel approach combining user similarity and network-based categorization to understand following motivations from social media data.

Findings

Similar followers and followees tend to be friends.

Different user categories exhibit distinct following behaviors.

The proposed method is feasible and effective in analyzing user intentions.

Abstract

Analyzing following behavior is important in many applications. Following behavior may depend on the main intention of the follower. Users may either follow their friends or they may follow celebrities to know more about them. It is difficult to estimate users' intention from their following relationships. In this paper, we propose an approach to analyze following relationships. First, we investigated the similarity between users. Similar followers and followees are likely to be friends. However, when the follower and followee are not similar, it is likely that follower seeks to obtain more information on the followee. Second, we categorized users by the network structure. We then proposed analysis of following behavior based on similarity and category of users estimated from tweets and user data. We confirmed the feasibility of the proposed method through experiments. Finally, we examined users in different categories and analyzed their following behavior.