What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections
| Autor: | Christine Sowa Lepird, Lynnette Hui Xian Ng, Anna Wu, Kathleen M. Carley |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Social Media + Society, Vol 10 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2056-3051 20563051 |
| DOI: | 10.1177/20563051241245950 |
| Popis: | News journalism has evolved from traditional print media to social media, with a large proportion of readers consuming their news via digital means. Through an analysis of over 1.3 million posts across three social media platforms (Facebook, Twitter, Reddit) pertaining to the 2022 U.S. Midterm Elections, this analysis examines the difference in sharing patterns for four types of news sites—Real News, Local News, Low Credibility News, and Pink Slime. Through Platform-Based Analysis, this study observes that users across all platforms share Real and Local News sequentially, and Real News and Low Credibility News sequentially. Through News Type-Based Analysis, this study establishes a Relative Engagement metric, demonstrating a widely varied engagement among the news types. Real News receive the least engagement (defined as the ratio of number of likes a post has vs. the number of followers of the page), while users engage with Pink Slime news the most. Furthermore, this study finds that the sharing of automated local news reporting sites (Pink Slime sites) are divided on political lines. Finally, through a User-Based Analysis, this study finds that automated bot users share a larger proportion of Pink Slime and Low Credibility News, while human users generally share content relating to local communities. |
| Databáze: | Directory of Open Access Journals |
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