Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse (2021)

Abstract: With 56 million people actively trading and investing in cryptocurrency online and globally in 2020, there is an increasing need for automatic social media analysis tools to help understand trading discourse and behavior. In this work, we present a dual natural language modeling pipeline which leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. This pipeline first predicts if tweets can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a profit. Next, tweets are input to an unsupervised deep clustering approach to automatically detect trading framing patterns. Our contributions include the modeling pipeline for this novel task, a new Cryptocurrency Tweets Dataset compiled from influential accounts, and a Historical Price Dataset. Our experiments show that our approach achieves an 88.78% accuracy for day trading behavior prediction and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.

## introduction beginning with the 2008 introduction of bitcoin (btc) @xcite , a cryptocurrency for a peer-to-peer cash system, the use of cryptocurrencies and their corresponding blockchains have increasingly gained in popularity. in 2019, the number of americans owning cryptocurrency doubled from 7% in 2018 to 14%, representing about 35 million people trading and investing with cryptocurrency @xcite . this increase is largely due to the capability of cryptocurrency to improve various applications ranging from increased security of smart contracts to facilitating less expensive, faster cross-border international payments. another contributing factor to this growth is that digital coins fulfill the propmodeling social media microblogs, specifically 114 twitter, to show connections between online dis-115 course and its effects on public opinion has been 116 widely studied in nlp @xcite @xcite ; hasan and 118 3 https://github.com/msu-nlp-css/crypto-framing @xcite @xcite and the social sciences @xcite @xcite @xcite . there are many works on twitter sentiment analysis, but closest to our work are those concerning the use of twitter sentiment for stock market predictions @xcite @xcite . there are relatively few works concerning cryptocurrency analysis and prediction. of these, a majority use social media sentiment @xcite , volume of tweets @xcite , or both @xcite as the main feature for prediction. furthermore, the prediction tasks are typically to predict prices or whether those prices will rise or fall. however, sentiment is known to be difficult to predict on twitter. furthermore, the volume of tweets can be falsely inflated by bots reporting currency prices, but not contributing to the discourse. therefore, instead of sentiment or tweet volume, we aim to use the language directly extracted from tweets, their context, and features representing the social network behavior for a buy, sell, or hold investment action prediction. research has also shown that given an adequate amount of historical data, such as stock values and indices, it is possible to forecast future currency exchanges @xcite . different from this work, we focus on predicting cryptocurrency investment actions, instead of fiat currency prices, by extracting patterns from historical tweets rather than stock values and indices. previous works have shown the effectiveness of using frames to predict various social sciences phenomena, such as political framing of twitter discourse, congressional speeches, and news coverage of current events @xcite @xcite @xcite @xcite . framing has also been used to understand the role of twitter discussions in influencing public opinion of events such as riots and protests @xcite @xcite . despite this coverage, to the best of our knowledge we are the first to study the role of framing in economics, specifically concerning stocks or cryptocurrency day trading, or associated correlations with the current pandemic. this work presents a first step in understanding both cryptocurrency day trading and how framing can reveal insights about cryptocurrency trading. ## data annotation this section describes the collection and preprocessing steps of the tweets and historical bitcoin (btc) transaction prices. section 3.4 describes how tweets were annotated for use in the day trading behavior prediction model. the non-annotated version of these tweets were used in the framing clustering models. ## qualitative results in this section, we explore how cryptocurrency frames change over time and their correlation with cryptocurrency day trading behavior. section 6.1 ## conclusion we have presented a dual modeling pipeline to understand how the way influential people and news sources frame cryptocurrency discussions on twitter affects cryptocurrency day trading. using classic nlp techniques and cosine similarity between the distilbert representations of tweet features and cryptocurrency tweets, we provide a day trading prediction model that is capable of distinguishing between day trading actions such as buy, sell, or hold. using our features and modeling approach we are able to achieve an accuracy of 88.78% over a 49.72% traditional baseline. furthermore, we are first to present an unsupervised deep clustering approach to reveal the latent frames used to discuss these day trading behaviors. our work shows interesting relationships between investment actions and how cryptocurrency discussions are framed on twitter, as well as how these framing patterns change in response to a pandemic. groups influencers, media, and companies.