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

Hayato Oshimo, Shiori Hironaka, Mitsuo Yoshida, Kyoji Umemura

TL;DR

This paper proposes an approach to analyze following relationships based on similarity and category of users estimated from tweets and user data, and confirmed the feasibility of the proposed method through experiments.

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.

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

TL;DR

This paper proposes an approach to analyze following relationships based on similarity and category of users estimated from tweets and user data, and confirmed the feasibility of the proposed method through experiments.

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.
Paper Structure (14 sections, 6 equations, 3 figures, 3 tables)

This paper contains 14 sections, 6 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. User Categories on Social Media
  4. Purpose and Intention of Following
  5. Homophily on Social Media
  6. Data Collection
  7. User Classification and User Vector
  8. Follower--Followee Ratio
  9. User Classification Using Follower--Followee Ratio
  10. User Vector
  11. Definition of Similarity Between Users
  12. Analysis
  13. Discussion
  14. Conclusion

Figures (3)

  • Figure 1: Research workflow
  • Figure 2: Examples of users with high and low follower--followee ratio.
  • Figure 3: Normalized histograms of similarity across user categories.