{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:38:07Z","timestamp":1772041087796,"version":"3.50.1"},"reference-count":167,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000155","name":"Social Sciences and Humanities Research Council","doi-asserted-by":"publisher","award":["430-2020-0863"],"award-info":[{"award-number":["430-2020-0863"]}],"id":[{"id":"10.13039\/501100000155","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Price prediction tools play a significant role in small investors\u2019 behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies\u2014particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter.<\/jats:p>","DOI":"10.3390\/a17060234","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T03:48:49Z","timestamp":1716868129000},"page":"234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-0645","authenticated-orcid":false,"given":"Hossein","family":"Zolfagharinia","sequence":"first","affiliation":[{"name":"Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Najafi","sequence":"additional","affiliation":[{"name":"Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shamir","family":"Rizvi","sequence":"additional","affiliation":[{"name":"Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aida","family":"Haghighi","sequence":"additional","affiliation":[{"name":"School of Occupational and Public Health, Faculty of Community Services, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"ref_1","unstructured":"Edwards, J. 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