Abstract
A central goal of news research is to understand the interplay between news coverage and sociopolitical events. Although a great deal of work has elucidated how events drive news coverage, and how in turn news coverage influences societal outcomes, integrative systems-level models of the reciprocal interchanges between these two processes are sparse. Herein, we present a macro-scale investigation of the dynamic transactions between news frames and events using Hidden Markov Models (HMMs), focusing on morally charged news frames and sociopolitical events. Using 3,501,141 news records discussing 504,759 unique events, we demonstrate that sequences of frames and events can be characterized in terms of “hidden states” containing distinct moral frame and event relationships, and that these “hidden states” can forecast future news frames and events. This work serves to construct a path toward the integrated study of the news-event cycle across multiple research domains.
| Original language | English |
|---|---|
| Pages (from-to) | 335-355 |
| Number of pages | 21 |
| Journal | Journal of Communication |
| Volume | 70 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Jun 2020 |
Bibliographical note
Publisher Copyright:© The Author(s) 2020. Published by Oxford University Press on behalf of International Communication Association. All rights reserved.
Keywords
- Big Data
- Computational Communication Research
- Cross-Disciplinary Research
- Framing
- GDELT
- Hidden Markov Models
- MIME
- Moral Intuitions
- Online News