Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel

Abstract

Digital humanities is an important subject because it enables developments in history, literature, and films. In this article, we perform an empirical study of a Chinese historical text, Records of the Three Kingdoms (Records), and a historical novel of the same story, Romance of the Three Kingdoms (Romance). We employ deep-learning-based natural language processing (NLP) techniques to extract characters and their relationships. The adopted NLP approach can extract 93% and 91% characters that appeared in the two books, respectively. Then, we characterize the social networks and sentiments of the main characters in the historical text and the historical novel. We find that the social network in Romance is more complex and dynamic than that of Records, and the influence of the main characters differs. These findings shed light on the different styles of storytelling in the two literary genres and how the historical novel complicates the social networks of characters to enrich the literariness of the story.