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Fake news and propaganda: Trump's Democratic America and Hitler's National Socialist (Nazi) Germany

Author

Listed:
  • David E. Allen

    (School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan, and School of Business and Law, Edith Cowan University, Australia.)

  • Michael McAleer

    ( Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)

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

  • David E. Allen & Michael McAleer, 2019. "Fake news and propaganda: Trump's Democratic America and Hitler's National Socialist (Nazi) Germany," Documentos de Trabajo del ICAE 2019-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:1916
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    References listed on IDEAS

    as
    1. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
    2. 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.
    3. 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.
    4. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    5. 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.
    6. 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.
    7. David E. Allen & Michael McAleer & David McHardy Reid, 2018. "Fake News And Indifference To Truth: Dissecting Tweets And State Of The Union Addresses By Presidents Obama And Trump," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 180-203, December.
    8. Ejis, 2018. "Table of Contents," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 02, June.
    9. 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.
    10. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    11. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    12. 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.
    13. David E. Allen & Michael McAleer, 2018. "President Trump Tweets Supreme Leader Kim Jong-Un on Nuclear Weapons: A Comparison with Climate Change †," Sustainability, MDPI, vol. 10(7), pages 1-6, July.
    14. Allen, D.E. & McAleer, M.J., 2018. "Fake News and Indifference to Scientific Fact," Econometric Institute Research Papers TI 2018-054/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. 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.
    16. Ejis, 2018. "Table of Contents," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 01, March.
    17. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Text Mining; Sentiment Analysis; Word Cloud; Emotional Valence.;
    All these keywords.

    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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