Identification of Profiles on Multiple Social Networks

• Mariana Barreto Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
• Sérgio Lifschitz Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

DOI: https://doi.org/10.5753/sbbd_estendido.2024.243784

Abstract

This work presents the development of a web tool designed to identify user profiles on various social media platforms, such as Twitter, Facebook and Instagram, exclusively employing text similarity measures. The study focuses on using the Levenshtein distance, a metric known for measuring the difference between sequences of characters, to evaluate the similarity between usernames and profile names, seeking to determine the effectiveness of this approach in producing accurate results in identifying matching profiles between different platforms. The research conducts an analysis of the performance and accuracy of this metric, exploring and implementing strategies to improve these aspects. Furthermore, it describes the process of creating the web tool, highlighting how it facilitates the construction of a database that associates digital profiles with real individuals. The application of Levenshtein distance allows for a more efficient and rapid identification of connections between profiles from different social networks, enhancing the recognition and analysis of users' online presence in multiple digital environments.

Keywords: databases, levenshtein, social networks

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