IDEAS home Printed from https://ideas.repec.org/a/aag/wpaper/v22y2018i1p180-203.html
   My bibliography  Save this article

Fake News And Indifference To Truth: Dissecting Tweets And State Of The Union Addresses By Presidents Obama And Trump

Author

Listed:
  • David E. Allen

    (School of Mathematics and Statistics, University of Sydney, Australia)

  • Michael McAleer

    (Department of Finance, Asia University, Taiwan)

  • David McHardy Reid

    (Albers School of Business and Economics, Seattle University, Washington, USA)

Abstract

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, 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. President Trump's 2018 SOUA and his sample tweets are identified as being more positive in sentiment than President Obama's 2016 SOUA. This is confirmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

Suggested Citation

  • 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.
    • Handle: RePEc:aag:wpaper:v:22:y:2018:i:1:p:180-203
    as

    Download full text from publisher

    File URL: https://iads.site/Fake-News-and-Indifference-to-Truth-Dissecting-SOUA-by-Obama-and-Trump_September-2018
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. David E. Allen & Michael McAleer, 2019. "Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    3. David E. Allen & Michael McAleer, 2022. "Trump’s COVID-19 tweets and Dr. Fauci’s emails," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1643-1655, March.
    4. Massoud Moslehpour & Shin Hung Pan & Aviral Kumar Tiwari & Wing Keung Wong, 2021. "Editorial in Honour of Professor Michael McAleer," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(4), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David E. Allen & Michael McAleer, 2019. "Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    2. 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.
    3. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Documentos de Trabajo del ICAE 2014-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    4. David E. Allen & Michael McAleer, 2022. "Trump’s COVID-19 tweets and Dr. Fauci’s emails," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1643-1655, March.
    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. 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. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
    8. Felix Irresberger & Fee Elisabeth König & Gregor N. F. Weiß, 2017. "Crisis Sentiment in the U.S. Insurance Sector," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1295-1330, December.
    9. Xiaoman, Jin & Qing, Li & Jun, Wang & Jingmei, Zhao, 2023. "Voice or noise? Repetitive information and stock performance," Finance Research Letters, Elsevier, vol. 52(C).
    10. Rui Fan & Oleksandr Talavera & Vu Tran, 2018. "Does connection with @realDonaldTrump affect stock prices?," Working Papers 2018-07, Swansea University, School of Management.
    11. Ji Sun & Yi Zhou & Jiaguo (George) Wang & Jie (Michael) Guo, 2020. "Influence of media coverage and sentiment on seasoned equity offerings," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 557-585, April.
    12. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    13. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    14. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    15. Ding, Rong & Hou, Wenxuan & Liu, Yue (Lucy) & Zhang, John Ziyang, 2018. "Media censorship and stock price: Evidence from the foreign share discount in China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 112-133.
    16. Buehlmaier, Matthias M. M. & Zechner, Josef, 2016. "Financial media, price discovery, and merger arbitrage," CFS Working Paper Series 551, Center for Financial Studies (CFS).
    17. Benjamin Clapham & Michael Siering & Peter Gomber, 2021. "Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets," Information Systems Frontiers, Springer, vol. 23(2), pages 477-494, April.
    18. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media bots and stock markets," European Financial Management, European Financial Management Association, vol. 26(3), pages 753-777, June.
    19. Frank, Murray Z. & Sanati, Ali, 2018. "How does the stock market absorb shocks?," Journal of Financial Economics, Elsevier, vol. 129(1), pages 136-153.
    20. Alexander Kerl & Carolin Schürg & Andreas Walter, 2014. "The impact of Financial Times Deutschland news on stock prices: post-announcement drifts and inattention of investors," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(4), pages 409-436, November.

    More about this item

    Keywords

    Sentiment Analysis; Polarity; Bootstrapped t tests; Sign tests;
    All these keywords.

    JEL classification:

    • A1 - General Economics and Teaching - - General Economics
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aag:wpaper:v:22:y:2018:i:1:p:180-203. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vincent Pan (email available below). General contact details of provider: https://edirc.repec.org/data/dfasitw.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.