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
Figure C.4 visualizes the reduced form relationship between the FNC channel position (relative to the averaged MSNBC and CNN position) and local newspaper content similarity to FNC. In the left panel (a), the outcome and the instrument are residualized on state fixed effects. The right panel (b) additionally includes demographic controls, channel controls (share of households with access to each of the three channels), and generic newspaper language features (vocabulary size, average word length, average sentence length, and average article length). There is a clear downward relationship, suggesting that easier access to FNC is associated with more FNC-like content in the local county newspapers. Table C.5 presents reduced-form results in tabular format.
This paper is available on arxiv under CC 4.0 license.
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].