  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.
