Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/295979 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
CESifo Working Paper No. 10890
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
In this study, we propose a novel approach to detect supply-side media bias, independent of external factors like ownership or editors' ideological leanings. Analyzing over 100,000 articles from The New York Times (NYT) and The Wall Street Journal (WSJ), complemented by data from 22 million tweets, we assess the factors influencing article duration on their digital homepages. By flexibly controlling for demand-side preferences, we attribute extended homepage presence of ideologically slanted articles to supply-side biases. Utilizing a machine learning model, we assign "pro-Democrat" scores to articles, revealing that both tweets count and ideological orientation significantly impact homepage longevity. Our findings show that liberal articles tend to remain longer on the NYT homepage, while conservative ones persist on the WSJ. Further analysis into articles' transition to print and podcasts suggests that increased competition may reduce media bias, indicating a potential direction for future theoretical exploration.
Schlagwörter: 
media bias
media economics
social media
machine learning
JEL: 
D22
D72
D83
L82
Dokumentart: 
Working Paper
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