Table of Links 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 5.1. Main results 5.2. Robustness checks 6. Mechanisms and Heterogeneity 6.1. Local vs. national or international news content 6.2. Cable news media slant polarizes local newspapers 7. Conclusion and References Online Appendices A. Data Appendix A.1. Newspaper articles 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.4. Distribution of Fox News similarity in newspapers and B.5. Example articles by Fox News similarity 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.4. OLS results C.5. Reduced form results C.6. Sub-samples: Newspaper headquarters and other counties and C.7. Robustness: Alternative county matching C.8. Robustness: Historical circulation weights and C.9. Robustness: Relative circulation weights C.10. Robustness: Absolute and relative FNC viewership and C.11. Robustness: Dropping observations and clustering C.12. Mechanisms: Language features and topics C.13. Mechanisms: Descriptive Evidence on Demand Side C.14. Mechanisms: Slant contagion and polarization 6.2. Cable news media slant polarizes local newspapers Finally, we investigate effect heterogeneity regarding the prior partisan commitments of local newspapers. We distinguish three outlet groups based on 1996 U.S. presidential election endorsements: (1) those that endorsed the Republican candidate Bob Dole, (2) those that endorsed the Democrat candidate Bill Clinton, and (3) those that did not endorse either candidate. [18] Endorsements signal whether the pre-FNC/MSNBC political leaning of a newspaper was relatively conservative or liberal. The non-endorsers can be seen as politically neutral. [19] Table 4 re-estimates our main specification on different newspaper subsets. Column 1 focuses on Democrat-endorsing newspapers, where we find a significantly negative coefficient, suggesting that liberal-leaning newspapers become less FNC-like in their reporting when more exposed to FNC (relative to CNN/MSNBC). Column 3 subsets on Republican-endorsing newspapers. Those behave the same as the main sample, i.e., they adopt more FNC-like content. For newspapers with no (known) endorsements (column 2), no effect emerges Overall, exposure to FNC (and, to a lesser degree, CNN and MSNBC) seems to polarize local news content: Republican-leaning newspapers adopt right-wing cable news content in response to FNC viewership increases. Instead, Democrat-leaning papers become more left-wing. This dynamic is consistent with a market positioning effect (Mullainathan and Shleifer, 2005; Gentzkow et al., 2014), where the conservative papers accommodate FNC-viewers’ news preferences. In turn, liberal papers accommodate non-FNC-viewers’ news preferences. Such an ideological positioning is consistent with demand-side mechanisms. This paper is available on arxiv under CC 4.0 license. [18] This latter group includes newspapers where we could not find an explicit endorsement. [19] 1996 endorsements are pre-FNC/MSNBC and, thus, independent of channel positioning. [20] The reduced form coefficient signs reverse due to the negative first stage. A positive coefficient implies that a lower channel position leads to less slant contagion. Authors:
(1) Philine Widmer, ETH Zürich and philine.widmer@gess.ethz.ch;
(2) Sergio Galletta, ETH Zürich and sergio.galletta@gess.ethz.ch;
(3) Elliott Ash, ETH Zürich and ashe@ethz.ch. Table of Links Abstract and 1 Introduction 2. Data Abstract and 1 Introduction 2. Data 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3. Measuring Media Slant and 3.1. Text pre-processing and featurization 3.2. Classifying transcripts by TV source 3.2. Classifying transcripts by TV source 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 3.3. Text similarity between newspapers and TV stations and 3.4. Topic model 4. Econometric Framework 4.1. Instrumental variables specification 4.1. Instrumental variables specification 4.2. Instrument first stage and validity 4.2. Instrument first stage and validity 5. Results 5.1. Main results 5.1. Main results 5.2. Robustness checks 5.2. Robustness checks 6. Mechanisms and Heterogeneity 6.1. Local vs. national or international news content 6.1. Local vs. national or international news content 6.2. Cable news media slant polarizes local newspapers 6.2. Cable news media slant polarizes local newspapers 7. Conclusion and References 7. Conclusion and References Online Appendices Online Appendices A. Data Appendix A.1. Newspaper articles A.1. Newspaper articles A.2. Alternative county matching of newspapers and A.3. Filtering of the article snippets 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 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. 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.3. Human validation of NLP model B.4. Distribution of Fox News similarity in newspapers and B.5. Example articles by Fox News similarity B.4. Distribution of Fox News similarity in newspapers and B.5. Example articles by Fox News similarity B.6. Topics from the newspaper-based LDA model B.6. Topics from the newspaper-based LDA model C. Results Appendix C.1. First stage results and C.2. Instrument exogeneity C.1. First stage results and C.2. Instrument exogeneity C.3. Placebo: Content similarity in 1995/96 C.3. Placebo: Content similarity in 1995/96 C.4. OLS results C.4. OLS results C.5. Reduced form results C.5. Reduced form results C.6. Sub-samples: Newspaper headquarters and other counties and C.7. Robustness: Alternative county matching C.6. Sub-samples: Newspaper headquarters and other counties and C.7. Robustness: Alternative county matching C.8. Robustness: Historical circulation weights and C.9. Robustness: Relative circulation weights C.8. Robustness: Historical circulation weights and C.9. Robustness: Relative circulation weights C.10. Robustness: Absolute and relative FNC viewership and C.11. Robustness: Dropping observations and clustering C.10. Robustness: Absolute and relative FNC viewership and C.11. Robustness: Dropping observations and clustering C.12. Mechanisms: Language features and topics C.12. Mechanisms: Language features and topics C.13. Mechanisms: Descriptive Evidence on Demand Side C.13. Mechanisms: Descriptive Evidence on Demand Side C.14. Mechanisms: Slant contagion and polarization C.14. Mechanisms: Slant contagion and polarization 6.2. Cable news media slant polarizes local newspapers Finally, we investigate effect heterogeneity regarding the prior partisan commitments of local newspapers. We distinguish three outlet groups based on 1996 U.S. presidential election endorsements: (1) those that endorsed the Republican candidate Bob Dole, (2) those that endorsed the Democrat candidate Bill Clinton, and (3) those that did not endorse either candidate. [18] Endorsements signal whether the pre-FNC/MSNBC political leaning of a newspaper was relatively conservative or liberal. The non-endorsers can be seen as politically neutral. [19] Table 4 re-estimates our main specification on different newspaper subsets. Column 1 focuses on Democrat-endorsing newspapers, where we find a significantly negative coefficient, suggesting that liberal-leaning newspapers become less FNC-like in their reporting when more exposed to FNC (relative to CNN/MSNBC). Column 3 subsets on Republican-endorsing newspapers. Those behave the same as the main sample, i.e., they adopt more FNC-like content. For newspapers with no (known) endorsements (column 2), no effect emerges Overall, exposure to FNC (and, to a lesser degree, CNN and MSNBC) seems to polarize local news content: Republican-leaning newspapers adopt right-wing cable news content in response to FNC viewership increases. Instead, Democrat-leaning papers become more left-wing. This dynamic is consistent with a market positioning effect (Mullainathan and Shleifer, 2005; Gentzkow et al., 2014), where the conservative papers accommodate FNC-viewers’ news preferences. In turn, liberal papers accommodate non-FNC-viewers’ news preferences. Such an ideological positioning is consistent with demand-side mechanisms. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [18] This latter group includes newspapers where we could not find an explicit endorsement. [19] 1996 endorsements are pre-FNC/MSNBC and, thus, independent of channel positioning. [20] The reduced form coefficient signs reverse due to the negative first stage. A positive coefficient implies that a lower channel position leads to less slant contagion. Authors: (1) Philine Widmer, ETH Zürich and philine.widmer@gess.ethz.ch; (2) Sergio Galletta, ETH Zürich and sergio.galletta@gess.ethz.ch; (3) Elliott Ash, ETH Zürich and ashe@ethz.ch. Authors: Authors: (1) Philine Widmer, ETH Zürich and philine.widmer@gess.ethz.ch; (2) Sergio Galletta, ETH Zürich and sergio.galletta@gess.ethz.ch; (3) Elliott Ash, ETH Zürich and ashe@ethz.ch.

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Media Slant is Contagious: What We Can Conclude From This

Media Slant: Local vs. National or International News Content

MediaBias.TECH

Too Long; Didn't Read

Get Free Unlimited IP Geo Data With IPinfo Lite

Cable News Media Slant Polarizes Local Newspapers

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

Developing a Natural Language Understanding Model to Characterize Cable News Bias

Characterizing Cable News Bias: Related Research You Should Know About

Characterizing Cable News Bias: The Methodology You Have to Understand

What Were the Results of Our Study to Characterize Cable News Bias?

Characterizing Cable News Bias: Discussing What It All Means

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

Developing a Natural Language Understanding Model to Characterize Cable News Bias

Characterizing Cable News Bias: Related Research You Should Know About

Characterizing Cable News Bias: The Methodology You Have to Understand

What Were the Results of Our Study to Characterize Cable News Bias?

Characterizing Cable News Bias: Discussing What It All Means

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps