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. Mechanisms and Heterogeneity 6.1. Local vs. national or international news content An important question regarding slant contagion is what parts of the newspaper content shift towards cable TV content. Slant diffusion could work via production costs, such that local outlets borrow content (either directly or by picking up the stories that the channels cover). Alternatively (or additionally), slant could spill over into originally produced material. We study this question with local news – where direct borrowing of material is impossible, given the cable channels’ national focus. To distinguish local from non-local (national or international) news, we proceed as follows. We manually label each of the 128 topics from the newspaper corpus (see Section B.6) as more likely to cover local than non-local news. We then classify a newspaper article snippet as local news if, cumulatively, more than 50% of its topic share(s) cover topics labeled as local. We validate our approach via blind human annotations of 2,000 newspaper article snippets. The topic-based predictions come with an accuracy of 81% relative to the human annotation, suggesting that our approach performs well in identifying local articles. [15] Table 3 replicates our main regression specification but uses three alternative outcomes. Column 1 shows the effect of instrumented FNC viewership (relative to CNN and MSNBC) on the share of local news. There is no effect. Next, column 2 points to a positive and significant effect on non-local (national and international) news, with a coefficient even larger than our main estimate. The large effect on non-local content is intuitive given that these are the topics often covered by cable news outlets, so direct borrowing of content by local newspapers is possible (potentially in addition to slant spillovers into the newspaper’s own content). Finally, column 3 shows the effect on the slant of local news articles, for which direct borrowing is impossible. Nonetheless, there is a positive and significant effect, with point estimates almost identical to our main results. [16] Thus, slant contagion works on both local and non-local content. The latter effect is notable because it means that cable news influences the local outlets’ original content. This requires that cable TV shifts either readers’ or journalists’ preferences – or both. [17] This paper is available on arxiv under CC 4.0 license. [15] We recruited the annotators on Upwork, as or the human validation of the slant measure (see Section B.3). The annotators were not given any topic information; they read the newspaper article snippet and decided whether it covered local or other news. Since most news is local, we also check other metrics for the local category. The F1-score is 86%, while precision and recall are 88% and 84%, respectively. [16] Most of the article snippets are local news (71% according to human annotation, and 75% according to our topic-model-based categorization). Therefore, the sample underlying Table 3 is similar to the one in Table 2. [17] Appendix C.13 provides descriptive evidence on shifting reader preferences likely being a more salient mechanism than shifting journalist preferences. 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. Mechanisms and Heterogeneity 6.1. Local vs. national or international news content An important question regarding slant contagion is what parts of the newspaper content shift towards cable TV content. Slant diffusion could work via production costs, such that local outlets borrow content (either directly or by picking up the stories that the channels cover). Alternatively (or additionally), slant could spill over into originally produced material. We study this question with local news – where direct borrowing of material is impossible, given the cable channels’ national focus. To distinguish local from non-local (national or international) news, we proceed as follows. We manually label each of the 128 topics from the newspaper corpus (see Section B.6) as more likely to cover local than non-local news. We then classify a newspaper article snippet as local news if, cumulatively, more than 50% of its topic share(s) cover topics labeled as local. We validate our approach via blind human annotations of 2,000 newspaper article snippets. The topic-based predictions come with an accuracy of 81% relative to the human annotation, suggesting that our approach performs well in identifying local articles. [15] Table 3 replicates our main regression specification but uses three alternative outcomes. Column 1 shows the effect of instrumented FNC viewership (relative to CNN and MSNBC) on the share of local news. There is no effect. Next, column 2 points to a positive and significant effect on non-local (national and international) news, with a coefficient even larger than our main estimate. The large effect on non-local content is intuitive given that these are the topics often covered by cable news outlets, so direct borrowing of content by local newspapers is possible (potentially in addition to slant spillovers into the newspaper’s own content). Finally, column 3 shows the effect on the slant of local news articles, for which direct borrowing is impossible. Nonetheless, there is a positive and significant effect, with point estimates almost identical to our main results. [16] Thus, slant contagion works on both local and non-local content. The latter effect is notable because it means that cable news influences the local outlets’ original content. This requires that cable TV shifts either readers’ or journalists’ preferences – or both. [17] 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 [15] We recruited the annotators on Upwork, as or the human validation of the slant measure (see Section B.3). The annotators were not given any topic information; they read the newspaper article snippet and decided whether it covered local or other news. Since most news is local, we also check other metrics for the local category. The F1-score is 86%, while precision and recall are 88% and 84%, respectively. [16] Most of the article snippets are local news (71% according to human annotation, and 75% according to our topic-model-based categorization). Therefore, the sample underlying Table 3 is similar to the one in Table 2. [17] Appendix C.13 provides descriptive evidence on shifting reader preferences likely being a more salient mechanism than shifting journalist preferences. 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.

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Media Slant: Local vs. National or International News Content

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