A Stacking Integrated Learning Framework for Digital Currency Prediction Using Social Media Data

The development of the internet not only provides convenience for the majority of investors and users to invest in stocks, but also promotes the development of digital currencies through online social media. Although, social media data will also significantly affect the digital currency market, the number of digital currency investors is still very small, due to the lack of information acquisition channels for investors. From a practical point of view, it is valuable to study the relationship between social media data and digital currency and provide a basis for investors' decision-making. Therefore, it is necessary to combine data on multiple mainstream social media platforms to explore its impact on the performance of the digital currency market. Based on the stacking integrated learning framework, this paper studies the impact of social media data on the digital currency market.  To obtain the research sample data web crawler technology is used. The development of the influencing factors of data currency prices through the python self-encoding method is conducted. The stacking integrated learning framework used in this paper increases the model's prediction accuracy of sample data by 1.32%.