Social media and online data pose challenges in information mining, network analysis, opinion mining, and combating misinformation. However, no previous work has been able to apply knowledge graph (KG) and contextual focal structure analysis (CFSA) on multisource data to study situational awareness in public discussion and establish information propagation such as the Belt and Road Initiative (BRI).

This research uses multisource data, a knowledge graph model, and a CFSA, which we term KG-CFSA. We extract entities and topics from documents and correlate them with third-party data sources such as Wikidata and Diffbot. We establish relationships using a Cartesian product merge function to develop a graph model.

The merge function uses search algorithms and pairwise matching to establish relationships. The model is divided into three instances: document-entity, document-document, and topic-topic.

For the document-document instance, we used topics and entities and topic overlaps to establish a relationship while we used co-occurrence for the topic-topic instance.

The study identified 276 focal sets; the top two focal sets are focal sets 275 and 276. The most important focal content comes from an Indonesian Twitter user, who operates a personal blog on opinion and story covers. The findings highlight the effectiveness of multisource KG-CFSA in establishing context for a social network analysis.