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

PurposeThe 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...

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Bibliographic Details
Published in:Online information review 2023-01, Vol.47 (1), p.81-103
Main Authors: Amara, Amina, Hadj Taieb, Mohamed Ali, Ben Aouicha, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Blogs
Classification
Datasets
Deep learning
Digital media
Friendship
Information Transfer
Network Analysis
Profiles
Recommender systems
Reconciliation
Researchers
Semantics
Similarity
Social Media
Social networks
Sparsity
Taxonomy
Websites
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Summary:PurposeThe 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/approachWe 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.FindingsUsing 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/valueThis 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.
ISSN:1468-4527
1468-4535
DOI:10.1108/OIR-01-2021-0037