KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graph

Abstract

Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data from multiple social networks, knowledge graph fusion, and contextual focal structure analysis (CFSA) for an online study. This study has developed a multi-source graph model and applied a Cartesian merge to model relations across multiple documents, entities, and topics. We improved the information modeled with third-party data sources such as WikiData and DiffBot. This approach has created a multiplex network instance for CFSA detection, incorporating topic-topic, entity-entity, and document-document models. We applied this method to a dataset from the Indo-Pacific region and identified 40,000 unique focal sets of influential topics, entities, and documents. The top sets discussed economics, elections, and policies such as the Indo-Pacific Economic Framework, Ekonomi baru, #NKRIHargaMati, #IndonesiaJaya, and the Xinjiang Supply Chain. Our model tracks information spread across multiple social media platforms and enhances the visibility of vital information using various relationships. The results underscore the effectiveness of KG-CFSA in contextualizing large-scale information from multiple sources.