A Hybrid Model for Linking Multiple Social Identities Across Heterogeneous Online Social Networks

  title={A Hybrid Model for Linking Multiple Social Identities Across Heterogeneous Online Social Networks},
  author={Athanasios Kokkos and Theodoros Tzouramanis and Yannis Manolopoulos},
Automated online profiling consists of the accurate identification and linking of multiple online identities across heterogeneous online social networks that correspond to the same entity in the physical world. The paper proposes a hybrid profile correlation model which relies on a diversity of techniques from different application domains, such as record linkage and data integration, image and text similarity, and machine learning. It involves distance-based comparison methods and the… 

Figures from this paper

Short Paper: User Identification across Online Social Networks Based on Similarities among Distributions of Friends’ Locations
A user identification method conducted across online social networks using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names, to identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit.
Entity Resolution in Online Multiple Social Networks (@Facebook and LinkedIn)
  • Ravita Mishra
  • Computer Science
    Advances in Intelligent Systems and Computing
  • 2018
The application of analysis/findings will be helpful in marketing and job recruitment, where the manager wants to check the employee profile on Facebook and LinkedIn and the analysis/discovery can also be used in the security domain, recommendation system human resource management, and advertisement.
Capturing Deep Dynamic Information for Mapping Users across Social Networks
This paper presents a deep dynamic user mapping model that captures dynamic latent features from three aspects including posting pattern, writing pattern, and emotional fluctuation and develops a matching network that fuses dynamic and traditional features to identify accounts.
User Account Linkage Across Multiple Platforms with Location Data
A novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures, and a match score based on kernel density estimation combining both spatial andporal information is designed to retrieve the linked user accounts.


Learning a Probabilistic Semantic Model from Heterogeneous Social Networks for Relationship Identification
A generic approach that consists of an ontology-based social network integration approach and a statistic learning method towards the Semantic Web data and an analyzing approach that learns a probabilistic semantic model (PSM) from social data for relationship identification.
Matching entities across online social networks
HYDRA: large-scale social identity linkage via heterogeneous behavior modeling
HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.
User identification across multiple social networks
A method to identify users based on profile matching, which uses data from two popular social networks to study the similarity of profile definition and develops and demonstrates the effectiveness and efficiency of the tool in identifying and consolidating duplicated users on different websites.
On the Reliability of Profile Matching Across Large Online Social Networks
The extent to which the accuracy in practice is significantly lower than the one reported in prior literature is studied, by exploiting public attributes, i.e., information users publicly provide about themselves.
A robust gender inference model for online social networks and its application to LinkedIn and Twitter
Online social networking services have come to dominate the dot com world: Countless online communities coexist on the social Web. Some typically characteristic user attributes, such as gender, age
COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency
An efficient subgradient algorithm is developed to train the model by converting the original energy-based objective function into its dual form, and it is demonstrated that applying the integration results produced by the method can improve the accuracy of expert finding, an important task in social networks.
De-anonymizing Social Networks
A framework for analyzing privacy and anonymity in social networks is presented and a new re-identification algorithm targeting anonymized social-network graphs is developed, showing that a third of the users who can be verified to have accounts on both Twitter and Flickr can be re-identified in the anonymous Twitter graph.
Abusing Social Networks for Automated User Profiling
This paper describes how it is able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale, and automatically identify more than 1.2 million user profiles associated with these addresses.
Identifying Users across Different Sites using Usernames