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 5.2. Robustness checks Our first robustness checks rely on alternative samples. Table C.6 replicates the baseline estimates but only considers newspaper-county observations where the county coincides with where the immediate newspaper owner is based. The effect size doubles relative to the entire sample and is still significant despite a smaller sample. [13] Second, we use alternative weighting schemes. The results are robust to weighting by circulation from the pre-FNC/MSNBC era, specifically 1995 (Table C.9). So, our main results are not driven by potential circulation changes due to cable news exposure. [14] Next, we weight observations by relative circulation shares by county, multiplied by the number of surveyed individuals for each county by Nielsen (Table C.10). In using the number of surveyed individuals, we follow Martin and Yurukoglu (2017). Similarly, we also weight observations by relative circulation shares by county, multiplied by county population (Table C.11). The results are again positive and statistically significant. Third, we replicate the main results using different instrument specifications (Table C.12). Instead of FNC’s relative to CNN’s/MSNBC’s viewership combined, we take FNC viewership relative to CNN’s and MSNBC’s separately (columns 1/2 and columns 3/4, respectively), or just FNC viewership (columns 5/6). The estimates are positive and overall consistent with our main results, yet not significant in the FNC-vs-CNN specification. Fourth, we rely on a different matching of newspapers to counties. We assign each newspaper to a main county based on its name and other metadata, producing a larger sample but with less detailed circulation data (see Section A.2 for details). The results are fully robust (Appendix C.7). Finally, our results are still significant when clustering by state (Table C.13) and remain consistently significant when dropping newspapers individually (Figure C.5). This paper is available on arxiv under CC 4.0 license. [13] Meanwhile, the effect is smaller and insignificant when excluding headquarters counties (Table C.7). [14] Our main analysis does not use the pre-FNC/MSNBC county-level circulation data because it is available for fewer outlets, resulting in half the sample size. Our baseline specification (contemporary circulation weights) is robust to using the subsample where 1995 circulation is available. 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 5.2. Robustness checks Our first robustness checks rely on alternative samples. Table C.6 replicates the baseline estimates but only considers newspaper-county observations where the county coincides with where the immediate newspaper owner is based. The effect size doubles relative to the entire sample and is still significant despite a smaller sample. [13] Second, we use alternative weighting schemes. The results are robust to weighting by circulation from the pre-FNC/MSNBC era, specifically 1995 (Table C.9). So, our main results are not driven by potential circulation changes due to cable news exposure. [14] Next, we weight observations by relative circulation shares by county, multiplied by the number of surveyed individuals for each county by Nielsen (Table C.10). In using the number of surveyed individuals, we follow Martin and Yurukoglu (2017). Similarly, we also weight observations by relative circulation shares by county, multiplied by county population (Table C.11). The results are again positive and statistically significant. Third, we replicate the main results using different instrument specifications (Table C.12). Instead of FNC’s relative to CNN’s/MSNBC’s viewership combined, we take FNC viewership relative to CNN’s and MSNBC’s separately (columns 1/2 and columns 3/4, respectively), or just FNC viewership (columns 5/6). The estimates are positive and overall consistent with our main results, yet not significant in the FNC-vs-CNN specification. Fourth, we rely on a different matching of newspapers to counties. We assign each newspaper to a main county based on its name and other metadata, producing a larger sample but with less detailed circulation data (see Section A.2 for details). The results are fully robust (Appendix C.7). Finally, our results are still significant when clustering by state (Table C.13) and remain consistently significant when dropping newspapers individually (Figure C.5). 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 [13] Meanwhile, the effect is smaller and insignificant when excluding headquarters counties (Table C.7). [14] Our main analysis does not use the pre-FNC/MSNBC county-level circulation data because it is available for fewer outlets, resulting in half the sample size. Our baseline specification (contemporary circulation weights) is robust to using the subsample where 1995 circulation is available. 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: Local vs. National or International News Content

Media Slant is Contagious: The Main Results From Our Experiment

MediaBias.TECH

Too Long; Didn't Read

Make resilience your competitive advantage

Media Slant is Contagious: Robustness Checks to Verify Our Results

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Bias in Machine Learning Models for Depression Detection

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?

Bias in Machine Learning Models for Depression Detection

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?

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps