The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
Open accessAbstract
This paper describes early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts. We collected the twitter feeds for six months and got a randomized subsample of about one hundredth of the full volume of all tweets. We measured collective hope and fear on each day and analyzed the correlation between these indices and the stock market indicators. We found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
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Measuring user influence in twitter: The million follower fallacy
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Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
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Cited by
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
“In this paper we investigate whether public sentiment, as expressed in large-scale collections of daily Twitter posts, can be used to predict the stock market.”
BackgroundA systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547
A topology of Twitter research: disciplines, methods, and ethics
Zimmer, M., & Proferes, N. J. (2014) · Aslib Journal of Information Management · doi:10.1108/ajim-09-2013-0083 · Cited by 257
News or Noise? Using Twitter to Identify and Understand Company-specific News Flow
Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014) · Journal of Business Finance & Accounting · doi:10.1111/jbfa.12086 · Cited by 160
Anbaee Farimani, S., Vafaei Jahan, M., Milani Fard, A., & Tabbakh, S. R. K. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108742 · Cited by 93
Metadata
- Authors
- Zhang, X., Fuehres, H., & Gloor, P. A.
- Venue
- Procedia - Social and Behavioral Sciences
- Publisher
- Elsevier BV
- Published
- 2011
- Volume
- 26
- Pages
- 55-62
- ISSN
- 1877-0428 (ISSN-L)
- OpenAlex
- W2037625889
- Cited by
- 640
- References
- 2
- FWCI
- 67.60
- Type
- conference paper
- Language
- en
- Open access
- diamond
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by-nc-nd/3.0/ (vor, 927 d)
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- Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences. https://doi.org/10.1016/j.sbspro.2011.10.562
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
