User Personality Type Prediction based on Implicit Relations in Multiplex Networks

Articles in Press

Document Type : Original Article

Authors

1 Dept. of Computer Science, Faculty of Mathematical Science, University of Kashan, Kashan, Iran

2 Dept. of Computer Science, Faculty of Mathematical Science, Sharekord University, Sharekord, Iran

Abstract

Analyzing user behavior on social networks to understand psychological traits is of growing importance. Structuring data extracted from users' concurrent activity across various platforms as a multiplex network can provide valuable insights. However, conventional approaches are limited by their reliance on explicit relationships, which are often private, and their use of single-source data. Therefore, this paper proposes a novel two-stage framework for personality type prediction. Its primary innovation is the use of an implicit relationship network, extracted from the fusion of multi-source, multi-modal data include text, image, and location, instead of relying on explicit friendship links. In the first stage, community structures in the multiplex network are identified using a multi-objective evolutionary approach (MOEA/D-TS), and then the implicit relationship network is constructed using a link prediction method (CLPES). In the second stage, the resulting network is leveraged as an information source within a machine learning framework to predict user personality types. Evaluations demonstrate the proposed framework's significant superiority. The final model, using a Random Forest classifier with the fusion of all three data sources, achieved an average Macro-F1 score of 0.673. This represents an average performance improvement of 23% compared to single-source models. This finding confirms the value of analyzing implicit relations and fusing multi-source data for a deeper understanding of user personality.

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 24 August 2025

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History

  • Receive Date: 31 May 2025
  • Revise Date: 21 July 2025
  • Accept Date: 17 August 2025

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