User's Centrality Analysis for Home Location Estimation

TL;DR

This paper investigates how user centrality measures relate to the difficulty of estimating users' home locations, revealing that certain user types, like hub and authority users, are harder to analyze.

Contribution

It identifies the relationship between centrality scores and home location estimation difficulty, highlighting the characteristics of users who are challenging to analyze.

Findings

PageRank and HITS scores correlate with home location sharing among friends.

Users with higher HITS scores less frequently share home locations with friends.

Hub and authority users are more difficult to estimate in terms of home location.

Abstract

User attributes, such as home location, are useful for many applications. Many researchers have been tackling how to estimate users' home locations using relationships among users. It is known that the home locations of certain users, such as celebrities, are hard to estimate using relationships. However, because estimating the home locations of all celebrities is not actually hard, it is important to clarify the characteristics of users whose home locations are hard to estimate. We analyze whether centralities, which represent users' characteristics, and the tendency to have the same home locations as friends are related. The results indicate that PageRank and HITS scores are related to whether users have the same home location as friends, and that users with higher HITS scores have the same home location as their friends less often. This result indicates that there are two types of users whose home locations are difficult to estimate: hub users who follow many celebrities and authority users who are celebrities.