Into the Dynamics of Interpersonal Relationships from Diachronic Documents: Text-Based Temporal Social Network Construction

In this work, we propose an alternative approach for constructing temporal social networks from diachronic documents by leveraging both graph and textual information. Our framework utilizes a randomized relation extraction model to extract interpersonal relationships among people from long-form documents and then enhances the extracted social network through inference on a temporal knowledge graph. The effectiveness of our approach is demonstrated through experiments on two datasets, including a politician's diary and a newspaper archive, and it has potential applications in various interdisciplinary fields such as computational politics and computational history.