Al-Nasseri, A., Ali, F. M., & Tucker, A. (2021). Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors.
International Review of Financial Analysis, 78, 1-25. Retrieved from
https://www.sciencedirect.com/science/article/pii/S1057521921002362.
Alomari, M., Al Rababa’a, A. R., El-Nader, G., Alkhataybeh, A., & Rehman, M. U. (2021). Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK.
The Quarterly Review of Economics and Finance, 82, 280-297. Retrieved from
https://www.sciencedirect.com/science/article/pii/S1062976921001599.
Ando, T., Greenwood-Nimmo, M., & Shin, Y. (2022). Quantile connectedness: modeling tail behavior in the topology of financial networks.
Management Science, 68(4), 2401-2431. Retrieved from
https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2021.3984.
Atkins, A., Niranjan, M., & Gerding, E. (2018). Financial news predicts stock market volatility better than the closing price. The Journal of Finance and Data Science, 4(2), 120-137. Retrieved from
https://www.sciencedirect.com/science/article/pii/S240591881730048X.
Blankespoor, E., Miller, G. S., & White, H. D. (2014). The role of dissemination in market liquidity: Evidence from firms' use of Twitter™.
The Accounting Review, 89(1), 79-112. Retrieved from
https://publications.aaahq.org/accounting-review/article-abstract/89/1/79/3645.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market.
Journal of Computational Science, 2(1), 1-8. Retrieved from
https://www.sciencedirect.com/science/article/pii/S187775031100007X.
Bouri, E., Saeed, T., Vo, X. V., & Roubaud, D. (2021). Quantile connectedness in the cryptocurrency market.
Journal of International Financial Markets, Institutions and Money, 71, 42-65. Retrieved from
https://www.sciencedirect.com/science/article/pii/S1042443121000214.
Checkley, M. S., Higón, D. A., & Alles, H. (2017). The hasty wisdom of the mob: How market sentiment predicts stock market behavior.
Expert Systems with Applications, 77, 256-263. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0957417417300398
Corea, F. (2016). Can Twitter proxy the investors' sentiment? The case for the technology sector.
Big Data Research, 4, 70-74. Retrieved from
https://www.sciencedirect.com/science/article/pii/S2214579615300174.
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers.
International Journal of Forecasting, 28(1), 57-66. Retrieved from
https://www.sciencedirect.com/science/article/pii/S016920701100032X.
Dorouzi, E., & Shokri, N. (2020). Effect of Bitcoin on the Discipline of International Law.
The International Conference on Humanities and Law, Retrieved from
https://civilica.com/doc/718340/.
Fan, W., & Gordon, M. D. (2014). The power of social media analytics.
Communications of the ACM, 57(6), 74-81. Retrieved from
https://dl.acm.org/doi/fullHtml/10.1145/2602574.
García, D. (2013). Sentiment during recessions.
The Journal of Finance, 68(3), 1267-1300. Retrieved from
https://onlinelibrary.wiley.com/share/JYBX79QQW6DE9YKJNJNI?target=10.1111/jofi.12027.
Garcia, D., & Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin.
Royal Society Open Science, 2(9), 20-46. Retrieved from
https://royalsocietypublishing.org/doi/abs/10.1098/rsos.150288.
Groß-Klußmann, A., König, S., & Ebner, M. (2019). Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market.
Expert Systems with Applications, 136, 171-186. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0957417419304270.
Guo, F., Lyu, B., Lyu, X., & Zheng, J. (2025). Social Media Networks and Stock Price Synchronicity: Evidence from a Chinese Stock Forum.
Abacus, 61(2), 419-461. Retrieved from
https://onlinelibrary.wiley.com/doi/abs/10.1111/abac.12341.
Hajilo Moghadam, A., Shokri, N., & Zisti, S. (2023). Investigating the Spillover Effects of Selected Digital Currencies with an Emphasis on Their Impact on Gold in the Era Before and After the Covid-19 Pandemic and the Role of the Government (Applying the Wavelet Coherence Approach).
Public Sector Economics Studies, 2(2), 115-134. Retrieved from
https://pse.razi.ac.ir/article_2675.html?lang=en.
Karampatsas, N., Malekpour, S., Mason, A., & Mavis, C. P. (2023). Twitter investor sentiment and corporate earnings announcements.
European Financial Management, 29(3), 953-986. Retrieved from
https://onlinelibrary.wiley.com/doi/abs/10.1111/eufm.12384.
Kranefuss, E., & Johnson, D. K. (2021). Does Twitter Strengthen Volatility Forecasts? Evidence from the S&P 500, DJIA, and Twitter Sentiment Analysis.
Social Science Research Network (SSRN), Colorado College Working Paper, 2021-01, 1-30. Retrieved from
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3786251.
Mao, Y., Wei, W., Wang, B., & Liu, B. (2012). Correlating S&P 500 stocks with Twitter data. In
Proceedings of the first ACM international workshop on hot topics on interdisciplinary social networks research, 69-72. Retrieved from
https://dl.acm.org/doi/abs/10.1145/2392622.2392634.
Neri, F., Aliprandi, C., Capeci, F., & Cuadros, M. (2012). Sentiment analysis on social media.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (919-926). Retrieved from
https://ieeexplore.ieee.org/abstract/document/6425642/.
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of Twitter sentiment on stock price returns.
PloS one, 10(9), 1-21. Retrieved from
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138441.
Reboredo, J. C., & Ugolini, A. (2018). The impact of Twitter sentiment on renewable energy stocks.
Energy Economics, 76(C), 153-169. Retrieved from
https://www.sciencedirect.com/science/article/pii/S014098831830416X.
Shokri, N., Sahab Khodamoradi, M., and Hajiloo Moghadam, A. H. (2021). Investigating the effects of financial volatility spillover between digital currencies (application of multivariate GARCH approach).
Financial Management Perspective, 11(35), 143-172. Retrieved from
https://jfmp.sbu.ac.ir/article_102259.html?lang=en.
Shokri, N., & Roshanfekr, A. (2023). Investigating the spillover effects of Bitcoin's financial fluctuations on other digital currencies.
International Journal of Blockchains and Cryptocurrencies, 4(1), 65-79. Retrieved from
https://www.inderscienceonline.com/doi/abs/10.1504/IJBC.2023.131643.
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. Retrieved from
https://onlinelibrary.wiley.com/share/UUGNC78G44MHXBFZQ3RI?target=10.1111/j.1468-036X.2013.12007.x.
Sul, H. K., Dennis, A. R., & Yuan, L. (2017). Trading on Twitter: Using social media sentiment to predict stock returns.
Decision Sciences, 48(3), 454-488. Retrieved from
https://onlinelibrary.wiley.com/share/SVAHWSE6PIGINNVGXAER?target=10.1111/deci.12229.
Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals.
The Journal of Finance, 63(3), 1437-1467. Retrieved from
https://onlinelibrary.wiley.com/share/8YVNP3IEGRQ6KZTU7IPT?target=10.1111/j.1540-6261.2008.01362.x.
Timmermann, A. (2008). Elusive return predictability.
International Journal of Forecasting, 24(1), 1-18. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0169207007000969.
William, P., Shrivastava, A., Chauhan, P. S., Raja, M., Ojha, S. B., & Kumar, K. (2023). Natural Language processing implementation for sentiment analysis on tweets.
Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022 (317-327). Singapore: Springer Nature Singapore. Retrieved from
https://link.springer.com/chapter/10.1007/978-981-19-7982-8_26.
Zeitun, R., Rehman, M. U., Ahmad, N., & Vo, X. V. (2023). The impact of Twitter-based sentiment on US sectoral returns.
The North American Journal of Economics and Finance, 64, 1-16. Retrieved from
https://www.sciencedirect.com/science/article/pii/S1062940822001826.