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Fake News and Propaganda: Trump's Democratic America and Hitler's National Socialist (Nazi) Germany

Author

Listed:
  • Allen, D.E.
  • McAleer, M.J.

Abstract

This paper features an analysis of President Trump's two State of the Union addresses, which are analysed by means of various data mining techniques including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. In order to provide a contrast and some parallel context, analyses are also undertaken of President Obama's last State of the Union address and Hitler's 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than Hitler's relatively shorter address, which is characterized by a prevalence of negative emotions. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different

Suggested Citation

  • Allen, D.E. & McAleer, M.J., 2019. "Fake News and Propaganda: Trump's Democratic America and Hitler's National Socialist (Nazi) Germany," Econometric Institute Research Papers EI2019-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:115615
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    File URL: https://repub.eur.nl/pub/115615/EI2019-17.pdf
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    References listed on IDEAS

    as
    1. repec:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2847-y is not listed on IDEAS
    2. Leela Mitra & Gautam Mitra & Dan Dibartolomeo, 2009. "Equity portfolio risk estimation using market information and sentiment," Quantitative Finance, Taylor & Francis Journals, vol. 9(8), pages 887-895.
    3. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    4. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    5. repec:taf:applec:v:51:y:2019:i:30:p:3212-3235 is not listed on IDEAS
    6. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    7. repec:gam:jsusta:v:10:y:2018:i:7:p:2310-:d:156124 is not listed on IDEAS
    8. Allen, D.E. & McAleer, M.J. & McHardy Reid, D., 2018. "Fake News and Indifference to Truth," Econometric Institute Research Papers EI2018-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    10. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine News and Volatility: The Dow Jones Industrial Average and the TRNA Sentiment Series," Tinbergen Institute Discussion Papers 14-014/III, Tinbergen Institute.
    11. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    12. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    13. David E. Allen & Michael McAleer, 2018. "Fake news and indifference to scientific fact: President Trump’s confused tweets on global warming, climate change and weather," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 625-629, October.
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    More about this item

    Keywords

    Text Mining; Sentiment Analysis; Word Cloud; Emotional Valence;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • D79 - Microeconomics - - Analysis of Collective Decision-Making - - - Other

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