{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:52:56Z","timestamp":1771051976037,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.<\/jats:p>","DOI":"10.3390\/a16120542","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T11:54:48Z","timestamp":1701086088000},"page":"542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6324-8108","authenticated-orcid":false,"given":"Loris","family":"Belcastro","sequence":"first","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7469-6904","authenticated-orcid":false,"given":"Domenico","family":"Carbone","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-373X","authenticated-orcid":false,"given":"Cristian","family":"Cosentino","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7887-1314","authenticated-orcid":false,"given":"Fabrizio","family":"Marozzo","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5076-6544","authenticated-orcid":false,"given":"Paolo","family":"Trunfio","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"ref_1","first-page":"181","article-title":"Understanding cryptocurrencies","volume":"18","author":"Harvey","year":"2020","journal-title":"J. Financ. Econom."},{"key":"ref_2","first-page":"715","article-title":"Cryptocurrency, a successful application of blockchain technology","volume":"46","author":"Nishikawa","year":"2020","journal-title":"Manag. Financ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101188","DOI":"10.1016\/j.intfin.2020.101188","article-title":"The predictive power of public Twitter sentiment for forecasting cryptocurrency prices","volume":"65","author":"Kraaijeveld","year":"2020","journal-title":"J. Int. Financ. Mark. Inst. Money"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cakici, N., Fieberg, C., Metko, D., and Zaremba, A. (2023). Do Anomalies Really Predict Market Returns? New Data and New Evidence. Rev. Financ. Forthcom., rfad025.","DOI":"10.1093\/rof\/rfad025"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1093\/rfs\/hhaa009","article-title":"Empirical asset pricing via machine learning","volume":"33","author":"Gu","year":"2020","journal-title":"Rev. Financ. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1093\/rfs\/hhaa062","article-title":"Bond risk premiums with machine learning","volume":"34","author":"Bianchi","year":"2021","journal-title":"Rev. Financ. Stud."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3548","DOI":"10.1093\/rfs\/hhad017","article-title":"Option return predictability with machine learning and big data","volume":"36","author":"Bali","year":"2023","journal-title":"Rev. Financ. Stud."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.61351\/mf.v1i1.2","article-title":"Forecasting the equity premium: Do deep neural network models work?","volume":"1","author":"Zhou","year":"2023","journal-title":"Mod. Financ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Valencia, F., G\u00f3mez-Espinosa, A., and Vald\u00e9s-Aguirre, B. (2019). Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy, 21.","DOI":"10.3390\/e21060589"},{"key":"ref_10","first-page":"1","article-title":"Cryptocurrency price prediction using tweet volumes and sentiment analysis","volume":"1","author":"Abraham","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Branda, F., Marozzo, F., and Talia, D. (2020). Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4040036"},{"key":"ref_12","first-page":"1121","article-title":"Comparative performance of machine learning algorithms for cryptocurrency forecasting","volume":"11","author":"Hitam","year":"2018","journal-title":"Ind. J. Electr. Eng. Comput. Sci"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/isaf.1488","article-title":"Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey","volume":"28","author":"Khedr","year":"2021","journal-title":"Intell. Syst. Account. Financ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"82804","DOI":"10.1109\/ACCESS.2020.2990659","article-title":"Stochastic neural networks for cryptocurrency price prediction","volume":"8","author":"Jay","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"102583","article-title":"A deep learning-based cryptocurrency price prediction scheme for financial institutions","volume":"55","author":"Patel","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.chaos.2018.11.014","article-title":"Cryptocurrency forecasting with deep learning chaotic neural networks","volume":"118","author":"Lahmiri","year":"2019","journal-title":"Chaos Solitons Fractals"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ammer, M.A., and Aldhyani, T.H. (2022). Deep learning algorithm to predict cryptocurrency fluctuation prices: Increasing investment awareness. Electronics, 11.","DOI":"10.3390\/electronics11152349"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102708","DOI":"10.1016\/j.ipm.2021.102708","article-title":"Global cryptocurrency trend prediction using social media","volume":"58","author":"Poongodi","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_19","first-page":"1","article-title":"Cryptocurrency price prediction using news and social media sentiment","volume":"1","author":"Lamon","year":"2017","journal-title":"SMU Data Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fleischer, J.P., von Laszewski, G., Theran, C., and Parra Bautista, Y.J. (2022). Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory. Algorithms, 15.","DOI":"10.3390\/a15070230"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Van Tran, L., Le, S.T., and Tran, H.M. (2022, January 20\u201322). Empirical Study of Cryptocurrency Prices Using Linear Regression Methods. Proceedings of the 2022 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/RIVF55975.2022.10013790"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, J., Zhou, Y., and Lin, J. (2019, January 6\u20139). Using machine learning for cryptocurrency trading. Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan.","DOI":"10.1109\/ICPHYS.2019.8780358"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3390\/ai2040030","article-title":"A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms","volume":"2","author":"Hamayel","year":"2021","journal-title":"AI"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Livieris, I.E., Pintelas, E., Stavroyiannis, S., and Pintelas, P. (2020). Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms, 13.","DOI":"10.3390\/a13050121"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rathan, K., Sai, S.V., and Manikanta, T.S. (2019, January 23\u201325). Crypto-currency price prediction using decision tree and regression techniques. Proceedings of the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI.2019.8862585"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TCSS.2021.3059286","article-title":"Identifying and analyzing cryptocurrency manipulations in social media","volume":"8","author":"Mirtaheri","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e12493","DOI":"10.1111\/exsy.12493","article-title":"Advanced social media sentiment analysis for short-term cryptocurrency price prediction","volume":"37","year":"2020","journal-title":"Expert Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"47","DOI":"10.18178\/ijke.2019.5.2.116","article-title":"Sentiment analysis of news for effective cryptocurrency price prediction","volume":"5","author":"Vo","year":"2019","journal-title":"Int. J. Knowl. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40854-020-00217-x","article-title":"Forecasting and trading cryptocurrencies with machine learning under changing market conditions","volume":"7","author":"Godinho","year":"2021","journal-title":"Financ. Innov."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hutto, C., and Gilbert, E. (2014, January 1\u20134). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"ref_31","first-page":"269","article-title":"textblob Documentation","volume":"2","author":"Loria","year":"2018","journal-title":"Release 0.16"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pano, T., and Kashef, R. (2020). A complete VADER-based sentiment analysis of bitcoin (BTC) tweets during the era of COVID-19. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4040033"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56232","DOI":"10.1109\/ACCESS.2022.3177888","article-title":"A deep learning-based cryptocurrency price prediction model that uses on-chain data","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"138633","DOI":"10.1109\/ACCESS.2021.3117848","article-title":"Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations","volume":"9","author":"Tanwar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"162651","DOI":"10.1109\/ACCESS.2021.3133937","article-title":"Improving the cryptocurrency price prediction performance based on reinforcement learning","volume":"9","author":"Shahbazi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00555-2","article-title":"Programming big data analysis: Principles and solutions","volume":"9","author":"Belcastro","year":"2022","journal-title":"J. Big Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.iref.2021.06.007","article-title":"Cryptocurrency price volatility and investor attention","volume":"76","year":"2021","journal-title":"Int. Rev. Econ. Financ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102654","DOI":"10.1016\/j.frl.2021.102654","article-title":"The link between cryptocurrencies and Google Trends attention","volume":"47","author":"Aslanidis","year":"2022","journal-title":"Financ. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101889","DOI":"10.1109\/ACCESS.2022.3209662","article-title":"HyVADRF: Hybrid VADER\u2013Random Forest and GWO for Bitcoin Tweet Sentiment Analysis","volume":"10","author":"Mardjo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_42","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased boosting with categorical features. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MCOM.2019.1800155","article-title":"Deep learning with long short-term memory for time series prediction","volume":"57","author":"Hua","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Moustafa, H., Malli, M., and Hazimeh, H. (2022, January 6\u20137). Real-time Bitcoin price tendency awareness via social media content tracking. Proceedings of the 2022 10th International Symposium on Digital Forensics and Security (ISDFS), Istanbul, Turkey.","DOI":"10.1109\/ISDFS55398.2022.9800793"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Maqsood, U., Khuhawar, F.Y., Talpur, S., Jaskani, F.H., and Memon, A.A. (2022, January 14\u201317). Twitter Mining based Forecasting of Cryptocurrency using Sentimental Analysis of Tweets. Proceedings of the 2022 Global Conference on Wireless and Optical Technologies (GCWOT), Malaga, Spain.","DOI":"10.1109\/GCWOT53057.2022.9772923"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/12\/542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:31:43Z","timestamp":1760131903000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/12\/542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["a16120542"],"URL":"https:\/\/doi.org\/10.3390\/a16120542","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]}}}