Abstract

Narratives are 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.

Metadata

publication

Proceedings of the International AAAI Conference on Web and Social Media 18 …, 2024

year

2024

publication date

2024/5/28

authors

Wanying Zhao, Siyi Guo, Kristina Lerman, Yong-Yeol Ahn

link

https://ojs.aaai.org/index.php/ICWSM/article/view/31427

resource_link

https://ojs.aaai.org/index.php/ICWSM/article/download/31427/33587

journal

Proceedings of the International AAAI Conference on Web and Social Media

volume

18

pages

1804-1817