User characterization for online social networks

TL;DR

This paper reviews methods for characterizing online social network users, focusing on attribute inference, behavior analysis, and user categorization to enhance understanding and service provision.

Contribution

It provides a comprehensive summary of techniques for user characterization, addressing challenges like identity deception and cross-platform matching.

Findings

Insights into user attribute determination methods

Analysis of motives behind user deception

Frameworks for user categorization and entity matching

Abstract

Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender, age, etc.; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots vs. humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights to researchers and better services to online users.