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Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

Richard Cruz1, Johnson Kinyua1,*, Charles Mutigwe2

1 College of Information Sciences and Technology, Pennsylvania State University, State College, PA, 16801, USA
2 Western New England University, College of Business, 1215 Wilbraham Rd, Springfield, MA, 01119, USA

* Corresponding Author: Johnson Kinyua. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 3405-3423.


The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models are used to try and predict the stock price movements of two meme stocks: GameStop ($GME) and AMC Entertainment ($AMC). Two sentiment metrics of the daily social media discussions about these stocks on Reddit are generated and used together with 85 other fundamental and technical indicators as the feature set for the machine learning models. It is demonstrated that through the use of a carefully chosen mix of a meme stock’s fundamental indicators, technical indicators, and social media sentiment scores, it is possible to predict the stocks’ next-day closing prices. Also, using an anomaly detection model, and the daily Reddit discussions about a meme stock, it was possible to identify potential market manipulators.


Cite This Article

R. Cruz, J. Kinyua and C. Mutigwe, "Analysis of social media impact on stock price movements using machine learning anomaly detection," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3405–3423, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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