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Social media sentiment and consumer confidence

Author

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  • Daas, Piet J.H.
  • Puts, Marco J.H.

Abstract

Changes in the sentiment of Dutch public social media messages were compared with changes in monthly consumer confidence over a period of three-and-a-half years, revealing that both were highly correlated (up to r = 0.9) and that both series cointegrated. This phenomenon is predominantly affected by changes in the sentiment of all Dutch public Facebook messages. The inclusion of various selections of public Twitter messages improved this association and the response to changes in sentiment. Granger causality studies revealed that it is more likely that changes in consumer confidence precede those in social media sentiment than vice-versa. A comparison of the development of various seven-day sentiment aggregates with the monthly consumer confidence series confirmed this finding and revealed that the social media sentiment lag is most likely in the order of seven days. This indicates that, because of the ease at which social media sentiment-based data are available and can be processed, they can be published before the official consumer confidence publication and certainly at a higher frequency. All research findings are consistent with the notion that changes in consumer confidence and social media sentiment are affected by an identical underlying phenomenon. An explanation for this phenomenon can be found in the Appraisal-Tendency Framework (Han et al. 2007), which is concerned with consumer decision-making. In this framework, it is claimed that a consumer decision is influenced by two kinds of emotions, namely the incidental and the integral. In this framework, the integral emotion is relevant for the decision at stake, whereas the incidental emotion is not. Based on this theory, consumer confidence is likely to be influenced mainly by the incidental emotion, as consumer confidence is also not measured in relation to an actual decision to buy something. This suggests that the sentiment in social media messages might reflect the incidental emotion in that part of the population that is active on social media. Because of the general nature of the latter, one could denote this the JEL Classification: C55

Suggested Citation

  • Daas, Piet J.H. & Puts, Marco J.H., 2014. "Social media sentiment and consumer confidence," Statistics Paper Series 5, European Central Bank.
    • Handle: RePEc:ecb:ecbsps:20145
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecbsp5.en.pdf
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    References listed on IDEAS

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    1. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
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    Cited by:

    1. Tolga Buz & Gerard de Melo, 2021. "Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach," Papers 2105.02728, arXiv.org.
    2. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2021. "Social Media and Twitter Data Quality for New Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 601-630, August.
    3. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    4. Daas Piet J.H. & Buelens Bart & Hurk Paul A.M. van den & Puts Marco J., 2015. "Big Data as a Source for Official Statistics," Journal of Official Statistics, Sciendo, vol. 31(2), pages 249-262, June.
    5. Bizzi, Lorenzo & Labban, Alice, 2019. "The double-edged impact of social media on online trading: Opportunities, threats, and recommendations for organizations," Business Horizons, Elsevier, vol. 62(4), pages 509-519.
    6. Heidi Kühnemann, 2021. "Anwendungen des Web Scraping in der amtlichen Statistik [Applications for web scraping in official statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 15(1), pages 5-25, March.
    7. Kang, Ju-Young M. & Johnson, Kim K.P., 2015. "F-Commerce platform for apparel online social shopping: Testing a Mowen’s 3M model," International Journal of Information Management, Elsevier, vol. 35(6), pages 691-701.
    8. Torres van Grinsven Vanessa & Snijkers Ger, 2015. "Sentiments and Perceptions of Business Respondents on Social Media: an Exploratory Analysis," Journal of Official Statistics, Sciendo, vol. 31(2), pages 283-304, June.
    9. Omotosho, Babatunde S. & Tumala, Mohammed M., 2019. "A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria," MPRA Paper 98850, University Library of Munich, Germany.
    10. Braaksma, Barteld & Zeelenberg, Kees, 2015. "“Re-make/Re-model”: Should big data change the modelling paradigm in official statistics?," MPRA Paper 87741, University Library of Munich, Germany.
    11. Fernando Ferri & Alessia D'Andrea & Patrizia Grifoni, 2017. "An Integrated Methodology for Approaching Sentiment Analysis in Business Domain," International Business Research, Canadian Center of Science and Education, vol. 10(9), pages 1-16, September.
    12. Hanjo Odendaal & Monique Reid & Johann F. Kirsten, 2020. "Media‐Based Sentiment Indices as an Alternative Measure of Consumer Confidence," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 409-434, December.
    13. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy (IfW Kiel).
    14. Omotosho, Babatunde S., 2020. "Central Bank Communication during Economic Recessions: Evidence from Nigeria," MPRA Paper 99655, University Library of Munich, Germany.
    15. Martina Patone & Li‐Chun Zhang, 2021. "On Two Existing Approaches to Statistical Analysis of Social Media Data," International Statistical Review, International Statistical Institute, vol. 89(1), pages 54-71, April.

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    More about this item

    Keywords

    big data; methodology; sentiment; social media; statistics;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    Statistics

    Access and download statistics

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