From Tweets to Trades: Enhancing Cryptocurrency Price Prediction through Multiplatform Sentiment and Explainable AI

From Tweets to Trades: Enhancing Cryptocurrency Price Prediction through Multiplatform Sentiment and Explainable AI | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 June 2025 V1 Latest version Share on From Tweets to Trades: Enhancing Cryptocurrency Price Prediction through Multiplatform Sentiment and Explainable AI Authors : Maimoona Maqsood Sharif [email protected] , Mustansar Ali Ghazanfar , Amin Karami , and Nadeem Qazi Authors Info & Affiliations https://doi.org/10.22541/au.175032245.55369563/v1 342 views 155 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This research presents a comprehensive study on cryptocurrency price prediction by integrating traditional financial modeling techniques with social media sentiment analysis. Considering the high volatility and sentiment driven nature of digital assets conventional models often fall short in delivering accurate forecasts. To address this gap, we conducted a multi-dimensional investigation encompassing a systematic review of 50 scholarly studies. an empirical analysis of over 25 million cryptocurrency related tweets and the application of quantitative methods including correlation matrices, Vector Autoregression (VAR) and Granger causality tests. Our findings reveal that social media metrics such as tweet volume, retweets, favorites and sentiment polarity serve as statistically significant predictors of market volatility. The correlation matrix confirms strong associations between sentiment indicators and price fluctuation while the VAR model and Granger causality analysis provide evidence of temporal and predictive causality. These results demonstrate that public discourse on platforms like Twitter can offer valuable foresight into market dynamics. The research further outlines practical implications for developing sentiment-enhanced predictive models including deep learning architectures (e.g., LSTM, GRU) and ensemble approaches (e.g., XGBoost). By validating the role of social sentiment in shaping market behavior this study contributes to the growing body of literature in behavioral finance, financial technology and data-driven trading. Ultimately, the proposed framework offers a forward-looking approach for academic researchers, financial practitioners and policymakers aiming to navigate and model the evolving landscape of cryptocurrency markets. Supplementary Material File (from tweets to trades (survey).docx) Download 230.83 KB Information & Authors Information Version history V1 Version 1 19 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords business news sentiment cryptocurrency machine learning nlp social media sentiment Authors Affiliations Maimoona Maqsood Sharif [email protected] University of East London School of Architecture Computing and Engineering View all articles by this author Mustansar Ali Ghazanfar University of East London School of Architecture Computing and Engineering View all articles by this author Amin Karami University of East London School of Architecture Computing and Engineering View all articles by this author Nadeem Qazi University of East London School of Architecture Computing and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 342 views 155 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Maimoona Maqsood Sharif, Mustansar Ali Ghazanfar, Amin Karami, et al. From Tweets to Trades: Enhancing Cryptocurrency Price Prediction through Multiplatform Sentiment and Explainable AI. Authorea . 19 June 2025. DOI: https://doi.org/10.22541/au.175032245.55369563/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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