Researching Media Slant: A Peek At Our OLS Results

Table C.4 shows OLS results for regressions of predicted Fox News similarity newspaper Slant on TV viewership.


This content originally appeared on HackerNoon and was authored by Tech Media Bias [Research Publication]

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

C.4. OLS results

\ Notes: OLS estimates. Cross-section with newspaper-county-level observations weighted by newspaper circulation in each county. The dependent variable is newspaper language similarity with FNC (the average probability that a snippet from a newspaper is predicted to be from FNC): F NC \jik=P(F NC|T extjik). In the first column, the righthand side variable of interest is FNC viewership relative to averaged CNN and MSNBC viewership. In the second column, it is FNC viewership relative to CNN viewership. In the third, it is FNC viewership relative to MSNBC viewership. Finally, in the fourth column, it is absolute FNC viewership. All columns include state fixed effects and demographic controls as listed in Appendix Table A.2, channel controls (population shares with access to each of the three TV channels), and controls for generic newspaper language features (vocabulary size, avg. word length, avg. sentence length, avg. article length). Standard errors are multiway-clustered at the county and at the newspaper level (in parenthesis): * p < 0.1, ** p < 0.05, *** p < 0.01.

\

:::info This paper is available on arxiv under CC 4.0 license.

:::

:::info 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.

:::

\


This content originally appeared on HackerNoon and was authored by Tech Media Bias [Research Publication]


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