Analysis of Home Location Estimation with Iteration on Twitter Following Relationship

TL;DR

This paper analyzes an iterative network-based method for estimating users' home locations on Twitter, finding that selecting the most frequent friends' location yields high accuracy and that two iterations are sufficient for effective estimation.

Contribution

The study provides an in-depth analysis of the iterative home location estimation method using Twitter following relationships, highlighting the effectiveness of majority voting among friends and the optimal number of iterations.

Findings

88% of users have at least one correct home location within one-hop.

Most frequent friends' location yields the best accuracy.

Two iterations are sufficient for effective estimation.

Abstract

User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration.