# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques  
[PERSON]  
1 Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON MSB 2K3, Canada; [EMAIL_ADDRESS] (M.N.J.) [EMAIL_ADDRESS] (S.R.) [PERSON]  
1 Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON MSB 2K3, Canada; [EMAIL_ADDRESS] (M.N.J.) [EMAIL_ADDRESS] (S.R.) [PERSON]  
1 Global Management Studies Department, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON 2K3, Canada; [EMAIL_ADDRESS] (M.N.J.) shamir.ri  
the need for reliable stock-price prediction models increases, especially as financial products and services become more accessible to smaller investors. Moreover, price prediction tools play a significant role in small investors' behavior. As such, numerous research studies (e.g., [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]) have been conducted to develop appropriate models and techniques that would widely be employed in algorithmic trading. Algorithmic trading, in essence, leverages computational power and mathematical formulas to illuminate buy or sell decisions for financial securities on an exchange and incorporates complex formulas and models with human oversight [24; 25; 26; 27]. These techniques are integral to institutional firms' trading algorithms, as they aid in minimizing transaction costs and market risks [28]. The rise of artificial intelligence (AI) in both the stock market and financial firms has significantly contributed to the growth of the algorithmic trading market. Companies like Sentient have developed AI-powered algorithmic traders, thereby showcasing the potential for these advanced algorithms to function as standalone entities [29; 30]. While numerous studies have developed models and techniques utilizing AI methods in stock-price prediction, they often focus on technical indices and general data in a normal market context. More specifically, these studies frequently overlook the impact of news on the decisions of small traders--particularly in market-panic scenarios. The profound impact of the media--most notably the news and Twitter--on investors' decisions in relation to stock buying and selling is evident. This has greatly shaped the modern financial landscape. News that is disseminated through traditional media or online platforms can swiftly influence investor sentiment by providing critical information about companies, industries, and the broader economic landscape. For example, during the COVID-19 pandemic, the demand for oil rapidly declined due to business closures and travel restrictions; this had, inevitably, caused the futures price for West Texas Intermediate (WTI) to plummet from USD 18 a barrel to around \(-\)USD 37 a barrel [31]. Real-time updates on corporate earnings, geopolitical events, and market trends can trigger immediate reactions and prompt investors to make rapid decisions in reassessing their positions. The social media platform Twitter, in particular, has become a dynamic space for financial discussions and for disseminating market-related information. Tweets from influential market analysts, financial experts, and even company executives can rapidly circulate and thereby influence investor perceptions and drive fluctuations in stock prices. The accessibility and speed of information on both news outlets and Twitter have made it imperative for investors to stay vigilant, as their decisions are now increasingly shaped by the instantaneous flow of news and opinions. Evidently, both the news and tweets can potentially impact small traders' trading decisions. As such, this study aims to investigate whether or not these parameters can affect price prediction performance. More specifically, we aim to answer the following research questions:  
* Can considering the news and tweets improve the stock-price prediction accuracy?