User Identification across Social Networking Sites using User Profiles and Posting Patterns
Prashant Solanki, Kwan Hui Lim, Aaron Harwood

TL;DR
This paper presents an algorithm that uses user profiles, posting patterns, and embeddings to identify the same user across different social networking sites, enabling better understanding of user behavior and improved recommendations.
Contribution
The study introduces a multilayer perceptron-based method leveraging diverse features for cross-OSN user identification, with an empirical analysis of feature effectiveness.
Findings
Profile features significantly improve identification accuracy
Temporal posting patterns contribute to matching performance
Embedding features enhance user similarity detection
Abstract
With the prevalence of online social networking sites (OSNs) and mobile devices, people are increasingly reliant on a variety of OSNs for keeping in touch with family and friends, and using it as a source of information. For example, a user might utilise multiple OSNs for different purposes, such as using Flickr to share holiday pictures with family and friends, and Twitter to post short messages about their thoughts. Identifying the same user across multiple OSNs is an important task as this allows us to understand the usage patterns of users among different OSNs, make recommendations when a user registers for a new OSN, and various other useful applications. To address this problem, we proposed an algorithm based on the multilayer perceptron using various types of features, namely: (i) user profile, such as name, location, description; (ii) temporal distribution of user generated…
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