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Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

Amina Amara (Data Engineering and Semantics Research Unit, Department of Computer Science, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia)
Mohamed Ali Hadj Taieb (Data Engineering and Semantics Research Unit, Department of Computer Science, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia)
Mohamed Ben Aouicha (Data Engineering and Semantics Research Unit, Department of Computer Science, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia)

Online Information Review

ISSN: 1468-4527

Article publication date: 4 May 2022

Issue publication date: 18 January 2023

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Abstract

Purpose

The intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.

Design/methodology/approach

We propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.

Findings

Using the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.

Originality/value

This paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

Keywords

Acknowledgements

In the interest of transparency, data sharing and reproducibility, the author(s) of this article have made the data underlying their research openly available. It can be accessed by following the link here: https://github.com/AnonymizedAccount/BUNetDataset-REST-API

Citation

Amara, A., Hadj Taieb, M.A. and Ben Aouicha, M. (2023), "Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users", Online Information Review, Vol. 47 No. 1, pp. 81-103. https://doi.org/10.1108/OIR-01-2021-0037

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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