Narrative is a foundation of human cognition and decision making. Because
narratives play a crucial role in societal discourses and spread of
misinformation and because of the pervasive use of social media, the narrative
dynamics on social media can have profound societal impact. Yet, systematic and
computational understanding of online narratives faces critical challenge of
the scale and dynamics; how can we reliably and automatically extract
narratives from massive amount of texts? How do narratives emerge, spread, and
die? Here, we propose a systematic narrative discovery framework that fill this
gap by combining change point detection, semantic role labeling (SRL), and
automatic aggregation of narrative fragments into narrative networks. We
evaluate our model with synthetic and empirical data two-Twitter corpora about
COVID-19 and 2017 French Election. Results demonstrate that our approach can
recover major narrative shifts that correspond to the major events.

本研究提出了一种系统的叙述性发现框架，通过改变点检测、语义角色标注（SRL）和叙事片段的自动聚合来解决大规模文本下叙述性的提取，并在两个 Twitter 语料库中评估了我们的模型，结果表明我们的方法可以恢复相应于重大事件的主要叙述转变。