User’s Centrality Analysis for Home Location Estimation

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. CCS CONCEPTS• Information systems → Web log analysis.

Cross-Country Analysis of User Profiles for Graph-Based Location Estimation

“…Some users who pose estimation difficulties have other characteristics. Hironaka et al [18] compared estimation difficulty with users' centrality and found that users with high hub and authority scores calculated by the HITS algorithm had difficult-to-estimate locations. Users with high authority scores are considered celebrities, whereas those users with high hub scores are the ones who follow many celebrities and constitute a different type of user.…”

“…We found that high follow_ratio is effective in all countries. A previous study [18] reported that location estimation is difficult in the cases of two types of users, hub users and authority users, as described in Section II. Authority users are considered to be celebrities or influencers.…”

Inferring Home Location of Foreign Tourists Based on Travel Routes Extracted from Social Media Sites

“…Hironaka et al 1 used data from Twitter to analyze users' home location based on their relationships with their friends. Hu et al 2 suggested a method to infer home location from sparse and noisy Twitter data within 100 by 100 meter squares at high accuracy using users' trajectory in their home country.…”