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Common Ground In Crisis: Causal Narrative Networks of Public Official Communications During the COVID-19 Pandemic

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arxiv 2309.03528 v1 pith:UQK6F3BU submitted 2023-09-07 cs.SI

Common Ground In Crisis: Causal Narrative Networks of Public Official Communications During the COVID-19 Pandemic

classification cs.SI
keywords causalcommunicationsdiscoursepublicagenciesconceptscovid-19narratives
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This study investigates the use of causal narratives in public social media communications by U.S. public agencies over the first fifteen months of the COVID-19 pandemic. We extract causal narratives in the form of cause/effect pairs from official communications, analyzing the resulting semantic network to understand the structure and dependencies among concepts within agency discourse and the evolution of that discourse over time. We show that although the semantic network of causally-linked claims is complex and dynamic, there is considerable consistency across agencies in their causal assertions. We also show that the position of concepts within the structure of causal discourse has a significant impact on message retransmission net of controls, an important engagement outcome.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence

    cs.CL 2026-05 unverdicted novelty 4.0

    The authors introduce a validation framework showing LLMs can pull causal links from disaster social media but require checks against post-event evidence to avoid relying on model priors.