Media Slant is Contagious: Topics From the Newspaper-Based LDA Model

Here are the topics from the newspaper-based lda model during our media bias analysis.


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

B.6. Topics from the newspaper-based LDA model

Notes: The 128 topics and their labels. The first columns shows the most frequent tokens for each topic. The second column lists the manually chosen topic labels. Sometimes, two or more topics are similar and receive the same label. For 22 out of 128 topics, no obvious label emerges. The last column captures whether we label the topic to be indicative of local rather than non-local (national, international) news. Table continued on the next page.

\ Notes: The 128 topics and their labels. The first columns shows the most frequent tokens for each topic. The second column lists the manually chosen topic labels. Sometimes, two or more topics are similar and receive the same label. For 22 out of 128 topics, no obvious label emerges. The last column captures whether we label the topic to be indicative of local rather than non-local (national, international) news. Table continued on the next page.

\ Notes: The 128 topics and their labels. The first columns shows the most frequent tokens for each topic. The second column lists the manually chosen topic labels. Sometimes, two or more topics are similar and receive the same label. For 22 out of 128 topics, no obvious label emerges. The last column captures whether we label the topic to be indicative of local rather than non-local (national, international) news.

\

:::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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