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Economic Uncertainty on Social Media: The Impact of X Posts on Economic and Financial Indicators

Year 2025, Volume: 7 Issue: 1, 56 - 69, 31.03.2025
https://doi.org/10.54821/uiecd.1634814

Abstract

This study explores the influence of posts on the social media platform X (formerly Twitter) concerning economic uncertainty on key economic and financial indicators, including GDP, commercial bank loans, and the New York Stock Exchange (NYSE). The analysis focuses on the United States due to its pivotal role in global financial markets and the significant presence of U.S. users, who account for 50% of English-speaking X users, offering a rich dataset for studying social media-driven economic sentiment. Variables such as the USA Gross Domestic Product Index, Commercial Bank Loans, New York Stock Exchange Composite, and X-based Economic Uncertainty Index (TEU) were analyzed using monthly data from June 2011 to April 2023. Employing a Vector Autoregressive (VAR) model, the study finds that fluctuations in commercial bank loans and the NYSE Composite significantly impact GDP, while posts reflecting economic uncertainty, as captured by the TEU, primarily respond to changes in bank loans. The results reveal a bidirectional relationship between GDP and commercial bank loans, where loans can drive economic growth through increased consumer spending and investment, though excessive borrowing may lead to instability and crises. Furthermore, the TEU is influenced solely by variations in commercial bank loans, highlighting social media sentiment’s sensitivity to credit dynamics in the U.S. economy.

References

  • Ahmed, G., & Watters, C. (2018). A study of the relationship between the geographic locations of the user and participation in Twitter during different types of news events. Transactions on Machine Learning and Artificial Intelligence, 6(6). https://doi.org/10.14738/tmlai.66.5636
  • Alshammari, A., Kapetanakis, S., Evans, R., Polatidis, N., & Alshammari, G. (2019). User modeling on Twitter with exploiting explicit relationships for personalized recommendations. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 135-145). https://doi.org/10.1007/978-3-030-14347-3_14
  • Alshammari, A., Kapetanakis, S., Polatidis, N., Evans, R., & Alshammari, G. (2019). Twitter user modeling based on indirect explicit relationships for personalized recommendations. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 93-105). https://doi.org/10.1007/978-3-030-28377-3_8
  • Ashraf, B. (2021). Is economic uncertainty a risk factor in bank loan pricing decisions? International evidence. Risks, 9(5), 81. https://doi.org/10.3390/risks9050081
  • Baker, S. R., Bloom, N., Davis, S., & Renault, T. (2021). Twitter-derived measures of economic uncertainty: Technical report.
  • Bai, P., Bai, Y., Safikhani, A., & Michailidis, G. (2021). Multiple change point detection in structured var models: the vardetect r package. arXiv preprint arXiv:2105.11007, https://doi.org/10.48550/arxiv.2105.11007
  • Bashir, F., & Wei, H. (2018). Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm. Neurocomputing, 276, 23-30. https://doi.org/10.1016/j.neucom.2017.03.097
  • Bertapelle, J., & Ballard-Reisch, D. (2015). Cultivating connections in 140 characters: A case study of Twitter relationship building. Media Watch, 6(3), 273. https://doi.org/10.15655/mw/2015/v6i3/77888
  • Bongomin, G., Ntayi, J., Munene, J., & Malinga, C. (2017). The relationship between access to finance and growth of SMEs in developing economies. Review of International Business and Strategy, 27(4), 520-538. https://doi.org/10.1108/ribs-04-2017-0037
  • Bringmann, L., Ferrer, E., Hamaker, E., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53(3), 293-314. https://doi.org/10.1080/00273171.2018.1439722
  • Bordo, M., Duca, J., & Koch, C. (2016). Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades. WP, 2016(1605). https://doi.org/10.24149/wp1605
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  • Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. (2018). VAR(1) based models do not always outpredict AR(1) models in typical psychological applications. Psychological Methods, 23(4), 740-756. https://doi.org/10.1037/met0000178
  • Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01540
  • Choi, M., Sang, Y., & Park, H. (2014). Exploring political discussions by Korean Twitter users. Aslib Journal of Information Management, 66(6), 582-602. https://doi.org/10.1108/ajim-11-2012-0089
  • Dablander, F., Ryan, O., & Haslbeck, J. (2020). Choosing between AR(1) and VAR(1) models in typical psychological applications. PLOS ONE, 15(10), e0240730. https://doi.org/10.1371/journal.pone.0240730
  • Deng, W., & Yang, Y. (2021). Cross-platform comparative study of public concern on social media during the COVID-19 pandemic: An empirical study based on Twitter and Weibo. International Journal of Environmental Research and Public Health, 18(12), 6487. https://doi.org/10.3390/ijerph18126487
  • Durani, F. (2024). Time-varying relationship between fossil fuel-free energy indices and economic uncertainty: Global evidence from wavelet coherence approach. International Journal of Energy Economics and Policy, 14(1), 663-672. https://doi.org/10.32479/ijeep.15257
  • Ghosh, A. (2016). Do real estate loans reflect regional banking and economic conditions? Journal of Financial Economic Policy, 8(1), 37-63. https://doi.org/10.1108/jfep-09-2015-0050
  • Gilbert, E., & Karahalios, K. (2010). Widespread worry and the stock market. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 58-65.
  • Gunarto, T., Ciptawaty, U., Russel, E., & Yuliawan, D. (2023). How is the modeling of the relationship between food ınflation and the agricultural sector composite stock price ındex with the statistical analysis system?. In International Conference of Economics, Business, and Entrepreneur (ICEBE 2022) (pp. 535-544). Atlantis Press. https://doi.org/10.2991/978-2-38476-064-0_54
  • Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and CrossSpectral Methods, Econometrica, 37, 424-438.
  • Grover, P., & Kar, A. (2020). Mining the social discussions surrounding circular economy: Insights from the collective intelligence shared in Twitter. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 303-314). https://doi.org/10.1007/978-3-030-64861-9_27
  • Haslbeck, J., Bringmann, L., & Waldorp, L. (2020). A tutorial on estimating time-varying vector autoregressive models. Multivariate Behavioral Research, 56(1), 120-149. https://doi.org/10.1080/00273171.2020.1743630
  • Hendry, D. F., & Juselius, K. (2001). Explaining cointegration analysis: Part II. The Energy Journal, 22(1), 75-120.
  • Hughes, D., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Computers in Human Behavior, 28(2), 561-569. https://doi.org/10.1016/j.chb.2011.11.001
  • Ibrahim, A., Kashef, R., Li, M., Valencia, E., & Huang, E. (2020). Bitcoin network mechanics: Forecasting the BTC closing price using vector auto-regression models based on endogenous and exogenous feature variables. Journal of Risk and Financial Management, 13(9), 189. https://doi.org/10.3390/jrfm13090189
  • Kılınç, G., Kocabiyik, T., & Karaatli, M. (2023). Baltic dry index estimation with NARX neural network model. Ekonomika, 102(1), 60-80. https://doi.org/10.15388/ekon.2023.102.1.4
  • Kim, W., & Chae, B. (2018). Understanding the relationship among resources, social media use and hotel performance. International Journal of Contemporary Hospitality Management, 30(9), 2888-2907. https://doi.org/10.1108/ijchm-02-2017-0085
  • Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? https://doi.org/10.1145/1772690.1772751
  • Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Evidence from accounting reports. The Journal of Finance, 66(1), 35-65.
  • Lu, M. (2001). Vector autoregression (VAR) — An approach to dynamic analysis of geographic processes. Geografiska Annaler Series B Human Geography, 83(2), 67-78. https://doi.org/10.1111/j.0435-3684.2001.00095.x
  • Lynn, T., Rosati, P., Nair, B., & Bhaird, C. (2020). An exploratory data analysis of the #crowdfunding network on Twitter. Journal of Open Innovation Technology Market and Complexity, 6(3), 80. https://doi.org/10.3390/joitmc6030080
  • Monchen, G., Sinquin, B., & Verhaegen, M. (2019). Recursive Kronecker-based vector autoregressive identification for large-scale adaptive optics. IEEE Transactions on Control Systems Technology, 27(4), 1677-1684. https://doi.org/10.1109/tcst.2018.2834521
  • Morina, F., & Özen, E. (2020). Does the commercial bank's loans affect economic growth? Empirical evidence for the real sector economy in Kosovo (2005-2018). International Journal of Sustainable Development and Planning, 15(8), 1205-1222. https://doi.org/10.18280/ijsdp.150807
  • Nazir, N., Bashir, Z., Izhar, S., & Jamshed, Y. (2023). Sources of uncertainty and their impact on stock prices evidence from emerging economies. Financial Internet Quarterly, 19(2), 49-67. https://doi.org/10.2478/fiqf-2023-0012
  • Pripoaie, R., Crețu, C., Turtureanu, A., Sîrbu, C., Marinescu, E., Talaghir, L., … & Robu, D. (2022). A statistical analysis of the migration process: A case study—Romania. Sustainability, 14(5), 2784. https://doi.org/10.3390/su14052784
  • Ranco, G., Aleksovski, D., Caldarelli, G., & Grčar, M. (2015). The effects of Twitter sentiment on stock price returns. PloS one, 10(9), e0138441.
  • Raunig, B., Scharler, J., & Sindermann, F. (2016). Do banks lend less in uncertain times? Economica, 84(336), 682-711. https://doi.org/10.1111/ecca.12211
  • Ruan, Y., Alfantoukh, L., & Durresi, A. (2015). Exploring stock market using Twitter trust network. https://doi.org/10.1109/aina.2015.217
  • Rusman Al, I.; Ruchjana, B. N. Sukono,(2019) Vector autoregressive (VAR) model for rain-fall forecasting in west java indonesia at the peak of the rainy season. International Journal of Recent Technology and Engineering, 8.2: 216-223.https://doi.org/10.35940/ijrte.b1054.0782s719
  • Sarıkovanlık, V., Koy, A., Akkaya, M., Yıldırım, H. H., & Kantar, L. (2019). Finans biliminde ekonometri uygulamaları. Ankara: Seçkin Yayıncılık.
  • Shultz, L. (2023). Taming global citizenship education within Twitter’s attention economy. Journal of Creative Communications, 19(1), 13-31. https://doi.org/10.1177/09732586231198960
  • Souza, J., Reisen, V., Franco, G., Ispány, M., Bondon, P., & Santos, J. (2017). Generalized additive models with principal component analysis: An application to time series of respiratory disease and air pollution data. Journal of the Royal Statistical Society Series C (Applied Statistics), 67(2), 453-480. https://doi.org/10.1111/rssc.12239
  • Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x
  • Valencia, F. (2013). Aggregate uncertainty and the supply of credit. IMF Working Paper, 13(241), 1. https://doi.org/10.5089/9781475513936.001
  • Yang, J. H., Kong, Q. Z., Liu, H. J., & Peng, H. Y. (2021). Efficient Bayesian model class selection of vector autoregressive models for system identification. Structural Control and Health Monitoring, 28(9), e2780. https://doi.org/10.1002/stc.2780
  • Yeşiltaş, S., Arslan, B., & Altuğ, S. (2022). A Twitter-based economic policy uncertainty index: Expert opinion and financial market dynamics in an emerging market economy. Frontiers in Physics, 10, 864207. https://doi.org/10.3389/fphy.2022.864207
  • Wu, W., Tiwari, A., Gözgör, G., & Huang, L. (2021). Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures. Research in International Business and Finance, 58, 101478. https://doi.org/10.1016/j.ribaf.2021.101478
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Sosyal Medyada Ekonomik Belirsizlik: X Gönderilerinin Ekonomik ve Finansal Göstergeler Üzerindeki Etkisi

Year 2025, Volume: 7 Issue: 1, 56 - 69, 31.03.2025
https://doi.org/10.54821/uiecd.1634814

Abstract

Bu çalışma, X (eski adıyla Twitter) sosyal medya platformunda ekonomik belirsizliğe dair paylaşımların, GSYİH, ticari banka kredileri ve New York Borsası (NYSE) gibi ekonomik ve finansal göstergeler üzerindeki etkisini incelemektedir. Analiz, ABD’ye odaklanmıştır çünkü ülke, küresel finansal piyasalardaki lider konumu ve İngilizce X kullanıcılarının %50’sini oluşturan geniş kullanıcı kitlesiyle, sosyal medya kaynaklı ekonomik duyarlılık çalışmalar için ideal bir örnek teşkil etmektedir. Çalışmada, USA Gross Domestic Product Index, Commercial Bank Loans, New York Stock Exchange Composite ve X tabanlı Ekonomik Belirsizlik Endeksi (TEU) değişkenleri, Haziran 2011 - Nisan 2023 dönemine ait aylık verilerle analiz edilmiştir. Vektör Otoregresif (VAR) model yapılan araştırmada, ticari banka kredileri ve NYSE’deki değişimlerin GSYİH’yi önemli ölçüde etkilediği, TEU ile ölçülen ekonomik belirsizlik paylaşımlarının ise esasen banka kredilerindeki dalgalanmalara tepki verdiği bulunmuştur. Bulgular, GSYİH ile ticari banka kredileri arasında çift yönlü bir ilişki olduğunu göstermektedir; krediler, tüketici harcamaları ve yatırımlar yoluyla büyümeyi teşvik edebilirken, aşırı borçlanma istikrarsızlık ve krizlere yol açabilir. Ayrıca, TEU’nun yalnızca banka kredilerindeki değişimlerden etkilenmesi, sosyal medya duyarlılığının ABD ekonomisindeki kredi dinamiklerine özel bir hassasiyet gösterdiğini ortaya koymaktadır.

References

  • Ahmed, G., & Watters, C. (2018). A study of the relationship between the geographic locations of the user and participation in Twitter during different types of news events. Transactions on Machine Learning and Artificial Intelligence, 6(6). https://doi.org/10.14738/tmlai.66.5636
  • Alshammari, A., Kapetanakis, S., Evans, R., Polatidis, N., & Alshammari, G. (2019). User modeling on Twitter with exploiting explicit relationships for personalized recommendations. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 135-145). https://doi.org/10.1007/978-3-030-14347-3_14
  • Alshammari, A., Kapetanakis, S., Polatidis, N., Evans, R., & Alshammari, G. (2019). Twitter user modeling based on indirect explicit relationships for personalized recommendations. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 93-105). https://doi.org/10.1007/978-3-030-28377-3_8
  • Ashraf, B. (2021). Is economic uncertainty a risk factor in bank loan pricing decisions? International evidence. Risks, 9(5), 81. https://doi.org/10.3390/risks9050081
  • Baker, S. R., Bloom, N., Davis, S., & Renault, T. (2021). Twitter-derived measures of economic uncertainty: Technical report.
  • Bai, P., Bai, Y., Safikhani, A., & Michailidis, G. (2021). Multiple change point detection in structured var models: the vardetect r package. arXiv preprint arXiv:2105.11007, https://doi.org/10.48550/arxiv.2105.11007
  • Bashir, F., & Wei, H. (2018). Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm. Neurocomputing, 276, 23-30. https://doi.org/10.1016/j.neucom.2017.03.097
  • Bertapelle, J., & Ballard-Reisch, D. (2015). Cultivating connections in 140 characters: A case study of Twitter relationship building. Media Watch, 6(3), 273. https://doi.org/10.15655/mw/2015/v6i3/77888
  • Bongomin, G., Ntayi, J., Munene, J., & Malinga, C. (2017). The relationship between access to finance and growth of SMEs in developing economies. Review of International Business and Strategy, 27(4), 520-538. https://doi.org/10.1108/ribs-04-2017-0037
  • Bringmann, L., Ferrer, E., Hamaker, E., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53(3), 293-314. https://doi.org/10.1080/00273171.2018.1439722
  • Bordo, M., Duca, J., & Koch, C. (2016). Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades. WP, 2016(1605). https://doi.org/10.24149/wp1605
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  • Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. (2018). VAR(1) based models do not always outpredict AR(1) models in typical psychological applications. Psychological Methods, 23(4), 740-756. https://doi.org/10.1037/met0000178
  • Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01540
  • Choi, M., Sang, Y., & Park, H. (2014). Exploring political discussions by Korean Twitter users. Aslib Journal of Information Management, 66(6), 582-602. https://doi.org/10.1108/ajim-11-2012-0089
  • Dablander, F., Ryan, O., & Haslbeck, J. (2020). Choosing between AR(1) and VAR(1) models in typical psychological applications. PLOS ONE, 15(10), e0240730. https://doi.org/10.1371/journal.pone.0240730
  • Deng, W., & Yang, Y. (2021). Cross-platform comparative study of public concern on social media during the COVID-19 pandemic: An empirical study based on Twitter and Weibo. International Journal of Environmental Research and Public Health, 18(12), 6487. https://doi.org/10.3390/ijerph18126487
  • Durani, F. (2024). Time-varying relationship between fossil fuel-free energy indices and economic uncertainty: Global evidence from wavelet coherence approach. International Journal of Energy Economics and Policy, 14(1), 663-672. https://doi.org/10.32479/ijeep.15257
  • Ghosh, A. (2016). Do real estate loans reflect regional banking and economic conditions? Journal of Financial Economic Policy, 8(1), 37-63. https://doi.org/10.1108/jfep-09-2015-0050
  • Gilbert, E., & Karahalios, K. (2010). Widespread worry and the stock market. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 58-65.
  • Gunarto, T., Ciptawaty, U., Russel, E., & Yuliawan, D. (2023). How is the modeling of the relationship between food ınflation and the agricultural sector composite stock price ındex with the statistical analysis system?. In International Conference of Economics, Business, and Entrepreneur (ICEBE 2022) (pp. 535-544). Atlantis Press. https://doi.org/10.2991/978-2-38476-064-0_54
  • Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and CrossSpectral Methods, Econometrica, 37, 424-438.
  • Grover, P., & Kar, A. (2020). Mining the social discussions surrounding circular economy: Insights from the collective intelligence shared in Twitter. In Proceedings of the International Conference on Computational Intelligence and Data Science (pp. 303-314). https://doi.org/10.1007/978-3-030-64861-9_27
  • Haslbeck, J., Bringmann, L., & Waldorp, L. (2020). A tutorial on estimating time-varying vector autoregressive models. Multivariate Behavioral Research, 56(1), 120-149. https://doi.org/10.1080/00273171.2020.1743630
  • Hendry, D. F., & Juselius, K. (2001). Explaining cointegration analysis: Part II. The Energy Journal, 22(1), 75-120.
  • Hughes, D., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Computers in Human Behavior, 28(2), 561-569. https://doi.org/10.1016/j.chb.2011.11.001
  • Ibrahim, A., Kashef, R., Li, M., Valencia, E., & Huang, E. (2020). Bitcoin network mechanics: Forecasting the BTC closing price using vector auto-regression models based on endogenous and exogenous feature variables. Journal of Risk and Financial Management, 13(9), 189. https://doi.org/10.3390/jrfm13090189
  • Kılınç, G., Kocabiyik, T., & Karaatli, M. (2023). Baltic dry index estimation with NARX neural network model. Ekonomika, 102(1), 60-80. https://doi.org/10.15388/ekon.2023.102.1.4
  • Kim, W., & Chae, B. (2018). Understanding the relationship among resources, social media use and hotel performance. International Journal of Contemporary Hospitality Management, 30(9), 2888-2907. https://doi.org/10.1108/ijchm-02-2017-0085
  • Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? https://doi.org/10.1145/1772690.1772751
  • Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Evidence from accounting reports. The Journal of Finance, 66(1), 35-65.
  • Lu, M. (2001). Vector autoregression (VAR) — An approach to dynamic analysis of geographic processes. Geografiska Annaler Series B Human Geography, 83(2), 67-78. https://doi.org/10.1111/j.0435-3684.2001.00095.x
  • Lynn, T., Rosati, P., Nair, B., & Bhaird, C. (2020). An exploratory data analysis of the #crowdfunding network on Twitter. Journal of Open Innovation Technology Market and Complexity, 6(3), 80. https://doi.org/10.3390/joitmc6030080
  • Monchen, G., Sinquin, B., & Verhaegen, M. (2019). Recursive Kronecker-based vector autoregressive identification for large-scale adaptive optics. IEEE Transactions on Control Systems Technology, 27(4), 1677-1684. https://doi.org/10.1109/tcst.2018.2834521
  • Morina, F., & Özen, E. (2020). Does the commercial bank's loans affect economic growth? Empirical evidence for the real sector economy in Kosovo (2005-2018). International Journal of Sustainable Development and Planning, 15(8), 1205-1222. https://doi.org/10.18280/ijsdp.150807
  • Nazir, N., Bashir, Z., Izhar, S., & Jamshed, Y. (2023). Sources of uncertainty and their impact on stock prices evidence from emerging economies. Financial Internet Quarterly, 19(2), 49-67. https://doi.org/10.2478/fiqf-2023-0012
  • Pripoaie, R., Crețu, C., Turtureanu, A., Sîrbu, C., Marinescu, E., Talaghir, L., … & Robu, D. (2022). A statistical analysis of the migration process: A case study—Romania. Sustainability, 14(5), 2784. https://doi.org/10.3390/su14052784
  • Ranco, G., Aleksovski, D., Caldarelli, G., & Grčar, M. (2015). The effects of Twitter sentiment on stock price returns. PloS one, 10(9), e0138441.
  • Raunig, B., Scharler, J., & Sindermann, F. (2016). Do banks lend less in uncertain times? Economica, 84(336), 682-711. https://doi.org/10.1111/ecca.12211
  • Ruan, Y., Alfantoukh, L., & Durresi, A. (2015). Exploring stock market using Twitter trust network. https://doi.org/10.1109/aina.2015.217
  • Rusman Al, I.; Ruchjana, B. N. Sukono,(2019) Vector autoregressive (VAR) model for rain-fall forecasting in west java indonesia at the peak of the rainy season. International Journal of Recent Technology and Engineering, 8.2: 216-223.https://doi.org/10.35940/ijrte.b1054.0782s719
  • Sarıkovanlık, V., Koy, A., Akkaya, M., Yıldırım, H. H., & Kantar, L. (2019). Finans biliminde ekonometri uygulamaları. Ankara: Seçkin Yayıncılık.
  • Shultz, L. (2023). Taming global citizenship education within Twitter’s attention economy. Journal of Creative Communications, 19(1), 13-31. https://doi.org/10.1177/09732586231198960
  • Souza, J., Reisen, V., Franco, G., Ispány, M., Bondon, P., & Santos, J. (2017). Generalized additive models with principal component analysis: An application to time series of respiratory disease and air pollution data. Journal of the Royal Statistical Society Series C (Applied Statistics), 67(2), 453-480. https://doi.org/10.1111/rssc.12239
  • Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x
  • Valencia, F. (2013). Aggregate uncertainty and the supply of credit. IMF Working Paper, 13(241), 1. https://doi.org/10.5089/9781475513936.001
  • Yang, J. H., Kong, Q. Z., Liu, H. J., & Peng, H. Y. (2021). Efficient Bayesian model class selection of vector autoregressive models for system identification. Structural Control and Health Monitoring, 28(9), e2780. https://doi.org/10.1002/stc.2780
  • Yeşiltaş, S., Arslan, B., & Altuğ, S. (2022). A Twitter-based economic policy uncertainty index: Expert opinion and financial market dynamics in an emerging market economy. Frontiers in Physics, 10, 864207. https://doi.org/10.3389/fphy.2022.864207
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There are 51 citations in total.

Details

Primary Language English
Subjects Time-Series Analysis
Journal Section Research Articles
Authors

Muhammed Fatih Yürük

Publication Date March 31, 2025
Submission Date February 6, 2025
Acceptance Date March 25, 2025
Published in Issue Year 2025 Volume: 7 Issue: 1

Cite

APA Yürük, M. F. (2025). Economic Uncertainty on Social Media: The Impact of X Posts on Economic and Financial Indicators. International Journal of Business and Economic Studies, 7(1), 56-69. https://doi.org/10.54821/uiecd.1634814


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