Discovering Collective Narratives Shifts in Online Discussions

• DOI: 10.1609/icwsm.v18i1.31427
• Publication Date: 2024-05-31T18:03:08Z
• Authors: Wanying Zhao; Siyi Guo; Kristina Lerman; Yong-Yeol Ahn
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; 0303 health sciences; 03 medical and health sciences; Computer Science - Computation and Language; Computer Science - Social and Information Networks; Computation and Language (cs.CL)

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