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.