Tweet Based Reach Aware Temporal Attention Network for NFT Valuation (2022)

Abstract: Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets. Designing strategies to forecast NFT trends is an intricate task due to its extremely volatile nature. The market is largely driven by public sentiment and “hype”, which in turn has a high correlation with conversations taking place on social media platforms like Twitter. Prior work done for modelling stock market data does not take into account the extent of impact certain highly influential tweets and their authors can have on the market. Building on these limitations and the nature of the NFT market, we propose a novel reach-aware temporal learning approach to make predictions for forecasting future trends in the NFT market. We perform experiments on a new dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections curated by us. Our model (TA-NFT) outperforms other state-of-the-art methods by an average of 36%. Through extensive quantitative and ablative analysis, we demonstrate the ability of our approach as a practical method for predicting NFT trends.

## introduction non fungible tokens (nfts) are digital assets that represent objects like art, collectibles, and in-game items 1 . public attention towards nfts exploded in 2021 when their market experienced record sales @xcite , but little is known about the overall structure and evolution of its market. the nft space is characterized by extreme growth along with highly skewed and uncertain returns that typify speculative markets @xcite . little to no work has been done to forecast future trends in the nft market, and unlike other, more stable assets, investing in nfts is associated with extremely high amounts of risk @xcite as they are highly volatile @xcite figure 1 : we visualize a sample of tweets related to bored ape yacht club nfts. we also plot the daily average price of the same nft collection to observe the impact of tweets by influential users. and lin, 2021). additionally, social media has emerged as a space for nft holders and creators to shape community opinion and drive public sentiment about nft projects @xcite . therefore, conventional forecasting approaches and contemporary ml models which utilize only numerical historic nft data fail to capture sufficient information. behavioral finance theories @xcite ) suggest that people are more likely to make decisions based on overconfidence bias @xcite and herd behavior @xcite when faced with uncertainty. the abundance of tweets about various nft collections help in creating "hype" around them which drives their sales, reinforcing herd behavior. studies have shown that nfts valued by experts are more successful @xcite , and that the structure of the the nft co-ownership network is highly centralized, and small-world-like @xcite . as shown in figure 1 , the daily average price of an nft collection, namely bored ape yacht club, reacts immediately to a highly influential individual tweeting positively about it and spikes up. however, numerous challenges arise while analyzing such texts. for instance, there are inherent dynamic timing irregularities @xcite when influencers or "alpha" users make such tweets and as communities react to them. simultaneously capturing temporal granularities along with popularity information @xcite is crucial, as more widely the content is shared over time, the greater the user's impact becomes @xcite . therefore, to develop a robust method for predicitng nft trends, we curate a dataset ( §3.1), and formulate a new time and popularity aware financial modelling approach, where the influence and reach of individual tweets is captured effectively translating its effects in their market value. our contributions can be summarized as: nlp in finance traditional financial forecasting techniques have been applied in areas such as stock markets @xcite , currency exchange markets @xcite , and energy economics @xcite . conventional financial models previously relied on numerical features @xcite and technical indicators @xcite . these include discrete @xcite , continuous @xcite , and neural approaches @xcite . efforts have since shifted towards utilizing textual data such as social media posts @xcite , news reports @xcite , web searches @xcite , etc., these studies confine their analyses to stock markets. recently, @xcite explored cryptocurrency bubble prediction based on user behavior on social media. however, there is a gap in leveraging social media and nlp to analyse and forecast future trends in the nft market. time-aware modelling temporal data is omnipresent in several real-world applications, including healthcare @xcite , recommender systems @xcite , and finance @xcite . as a result, sequential neural models such as lstms @xcite have gained popularity due to their ability to capture sequential context dependency @xcite . time-aware modelling of time series data has shown improvements over conventional sequential neural models on various tasks such as patient subtyping @xcite , suicide ideation detection @xcite , and disease progression @xcite . recently, timeaware modelling has been adapted in the realm of financial nlp, such as stock recommendation @xcite , price prediction @xcite , and ranking @xcite . however, these approaches do not take into account the engagement and popularity of social media posts. hence, such methods do not scale to nfts, which are more closely correlated with user sentiment and social media "hype" in comparison to traditional asset classes @xcite . with this work, we seek to explore a promising research avenue i.e the intersection of nfts and financial nlp, along with time and hype aware neural modelling. ## experimental setup preprocessing following (nguyen et al., 2020), we use nltk to preprocess tweets by converting mentions (@) and urls to special tokens @user and httpurl. we treat emoticons by converting them to strings using emoji python package. training setup we perform all our experiments on a tesla t4 gpu. we use optuna @xcite to find optimal hyperparameter values based on the validation mse/macro f1 scores by performing 25 search trials. we explored the lookback window length @xmath0, 40] and the hidden state @xmath1, 768]. we use 10%, 10% and 80% of the samples for testing, validation and training respectively for both tasks. we use learning @xmath2, 1e -2 ] and train the models using adam as our optimizer for 2,150 seconds and 10,845 seconds for daily average price prediction and price movement classification tasks, respectively. evaluation metrics we evaluate methods using mean squared error (mse) loss for daily average price prediction task and macro f1-score (m.f1) for price movement classification task. ## conclusion building on the rising popularity and hype-driven dynamics of nft markets, we curate a dataset for forecasting nft trends through two downstream tasks consisting of daily average price prediction and price movement classification. we introduced ta-nft, a time and reach-aware neural network for modelling temporal granularities and engagement dynamics of nft discourse on social media. through extensive experiments, we show that ta-nft empirically outperforms other sota models by an average of 36%, and present ta-nft as a practical modelling approach and a strong benchmark for forecasting nft trends. we hope the proposed dataset can enable more academic progress in the field of financial nlp.