Abstract and 1 Introduction 2. Data
3. Measuring Media Slant and 3.1. Text pre-processing and featurization
3.2. Classifying transcripts by TV source
3.3. Text similarity between newspapers and TV stations and 3.4. Topic model
4. Econometric Framework
4.1. Instrumental variables specification
4.2. Instrument first stage and validity
5. Results
6. Mechanisms and Heterogeneity
6.1. Local vs. national or international news content
6.2. Cable news media slant polarizes local newspapers
\ Online Appendices
A. Data Appendix
A.2. Alternative county matching of newspapers and A.3. Filtering of the article snippets
A.4. Included prime-time TV shows and A.5. Summary statistics
B. Methods Appendix, B.1. Text pre-processing and B.2. Bigrams most predictive for FNC or CNN/MSNBC
B.3. Human validation of NLP model
B.6. Topics from the newspaper-based LDA model
C. Results Appendix
C.1. First stage results and C.2. Instrument exogeneity
C.3. Placebo: Content similarity in 1995/96
C.8. Robustness: Historical circulation weights and C.9. Robustness: Relative circulation weights
C.12. Mechanisms: Language features and topics
C.13. Mechanisms: Descriptive Evidence on Demand Side
C.14. Mechanisms: Slant contagion and polarization
7. ConclusionWe document that partisan news messaging by large media organizations can spill over to smaller outlets. Specifically, the news messaging by U.S. cable TV channels influences local news reporting. Our evidence suggests that the newspapers shift the slant in their own local content, where direct copy-pasting from the TV channels is not possible (since the latter are nationally-oriented). Moreover, the exposure to the TV channels leads to a polarization of local news, where outlets that had already been pro-Republican in the pre-FNC/MSNBC era shift more towards FNC-like language. In contrast, outlets with a historically pro-Democrat leaning move towards CNN/MSNBClike language in response to higher FNC exposure. These findings add to concerns regarding increasing political polarization in the U.S. and beyond (e.g., Campbell, 2018; Carothers and O’Donohue, 2019).
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:::info This paper is available on arxiv under CC 4.0 license.
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:::info Authors:
(1) Philine Widmer, ETH Zürich and [email protected];
(2) Sergio Galletta, ETH Zürich and [email protected];
(3) Elliott Ash, ETH Zürich and [email protected].
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