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In January 2021, a redditor on r/wallstreetbets noticed that a hedge fund was betting against Gamestop by borrowing a large amount of stocks. He convinced participants on the thread to collectively buy as much Gamestop stock as possible. The price thus rose and the hedge funds short position started to lose billions. The hedge fund eventually filed for bankruptcy. This recent event demonstrated the glaring impact of social media on the stock market and the fact that
insights extracted from online buzz can cause huge losses or gains. That is the reason why many researchers have been striving lately to predict the markets while focusing on increasing the accuracy of their models. The dominant focus has been on sentiment analysis. However, we believe that sentiment is not the only meaningful feature. We therefore raise the question: what meaningful features can we extract when predicting stock prices based on social media data?
