Social Information Retrieval using Linked Data and Deep Learning

Online Social Networks (OSNs) are becoming increasingly important in business, government, and all
areas of life. For-profit companies use them as rich sources of information and dynamic platforms to drive
strategies in product design, innovation, relationship management, and marketing. However, analyzing
and retrieving information from these platforms presents distinct challenges due to their inherent
characteristics and dynamic nature. To address this, researchers have proposed various approaches for
social information retrieval, ranging from term-based analysis to semantic-based methods. To overcome
the limitations of existing techniques, the present study proposes a multilayer model that integrates graph
analysis, semantic content, and deep learning. The general proposed approach is also presented. By
combining learning-to-rank techniques with linked data, a robust framework for social information
retrieval is constructed. This method enables a more nuanced understanding by leveraging both the rich
contextual information provided by linked data and the structural characteristics of social networks. The
proposed model is a flexible framework that can be easily extended to add or remove features and can be
applied to various tasks. The experimental results confirm the effectiveness and efficiency of the proposed
approach.