A Transfer Learning Approach to Meme Token Classification: Combining Historical Cryptocurrency Data with Real-Time Social Signals

Meme tokens can rise and fall like sudden storms. Some appear out of nowhere and attract a crowd, while others vanish just as quickly. This paper presents a practical method for telling apart meme tokens that are likely to endure from those that are likely to fail or be used for harmful schemes. The method borrows knowledge learned from long lived cryptocurrency histories and applies that knowledge to new and fast moving meme tokens. At the same time the method listens to the crowd on social platforms in real time and uses those signals as a second view of what is happening. You can think of this as learning from old weather records and then watching live clouds to predict whether a storm will become a hurricane or just a rain shower. The main idea is to combine stable patterns from market history with instant clues from social activity so the model can make faster and more accurate judgments about new tokens. We test the method on several publicly available data sets and on a notebook based experiment you provided. The results show better detection accuracy and earlier warning times than simple models that use only market data or only social signals. We also explain which features help most when the system faces very new tokens with little trading history. The paper contributes a clear fusion strategy, a set of practical features for both market and social streams, and an evaluation on realistic token examples. The approach is transparent and ready for journal review.