Framing the Pandemic on Persian Twitter
Gauging Networked Frames by Topic Modeling
Dados Bibliográficos
| ID | 3758401 |
|---|---|
| Autores | H Kermani (0000-0002-6626-1364, University of Vienna, autor correspondente) |
| Ano | 2025 |
| Volume | 69 |
| Fascículo | 10 |
| Páginas | 1289-1304 |
| Data de publicação | 2025-09-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | American Behavioral Scientist (JOURNAL) |
| Identificadores do periódico | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Editora | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/00027642231207078 |
| OpenAlex | W4388047450 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 39 |
This study makes a dual contribution to the current literature. First, it examines how Iranian Twitter users framed the COVID-19 crisis in collaborative practice, networked framing. Second, it explores the potential for topic modeling in automated frame identification. The study analyzes a dataset of 4,165,177 tweets collected from Iranian Twittersphere between January 21, 2020 and April 29, 2020. The results indicate that Iranians predominantly framed the pandemic through a political lens and utilized anti-regime networked frames to contest the political system in general and during the pandemic. Furthermore, the study finds that while Latent Dirichlet Allocation (LDA) can accurately identify the most significant networked frames, it may overlook less prominent frames. The research also suggests that LDA performs better with larger datasets and lexical semantics. Lastly, the implications and limitations of the investigation are discussed
CONTEST · Coronavirus disease 2019 (COVID-19) · Data science · Framing (construction) · Geography · Latent Dirichlet allocation · Linguistics · Pandemic · Persian · Political science · Politics · Social media · Topic model · World Wide Web · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Law · Misinformation and Its Impacts · Social Media and Politics
Discourse Analysis as Theory and Method
Networks, Crowds, and Markets
Latent Dirichlet allocation (LDA) and topic modeling
A correlated topic model of Science
News framing
Rethinking Political Communication in a Time of Disrupted Public Spheres
Covid-19 Management in Iran as One of the Most Affected Countries in the World
Words matter
Dynamics of Networked Framing
Walking with Bourdieu into Twitter communities
Algorithmic Agents in the Hybrid Media System
What We Can Do and Cannot Do with Topic Modeling
Networked Gatekeeping and Networked Framing on #Egypt
Applying LDA Topic Modeling in Communication Research
Teaching the Computer to Code Frames in News
Building the Bridge
Portraying the Pandemic
Refugee debate and networked framing in the hybrid media environment
Topic modeling for frame analysis
(What) Can Journalism Studies Learn from Supervised Machine Learning
Exploiting affinities between topic modeling and the sociological perspective on culture
Coronavirus Pandemic
Through a different gate
Communicating Climate Change
Even in a global pandemic, there’s no such thing as a crisis
Computational Identification of Media Frames
Text as Data
Framing
An Overview of Lexical Semantics
The Future of Coding
Media Discourse and Public Opinion on Nuclear Power
| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 1 |
| Intervalo de citações | 2025 - 2025 (1) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |