@inproceedings{sawhney-etal-2022-tweet,
title = "Tweet Based Reach Aware Temporal Attention Network for {NFT} Valuation",
author = "Sawhney, Ramit and
Thakkar, Megh and
Soun, Ritesh and
Neerkaje, Atula and
Sharma, Vasu and
Guhathakurta, Dipanwita and
Chava, Sudheer",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.471",
doi = "10.18653/v1/2022.findings-emnlp.471",
pages = "6321--6332",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Tweet Based Reach Aware Temporal Attention Network for NFT Valuation
%A Sawhney, Ramit
%A Thakkar, Megh
%A Soun, Ritesh
%A Neerkaje, Atula
%A Sharma, Vasu
%A Guhathakurta, Dipanwita
%A Chava, Sudheer
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sawhney-etal-2022-tweet
%X 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.
%R 10.18653/v1/2022.findings-emnlp.471
%U https://aclanthology.org/2022.findings-emnlp.471
%U https://doi.org/10.18653/v1/2022.findings-emnlp.471
%P 6321-6332
Markdown (Informal)
[Tweet Based Reach Aware Temporal Attention Network for NFT Valuation](https://aclanthology.org/2022.findings-emnlp.471) (Sawhney et al., Findings 2022)
ACL
- Ramit Sawhney, Megh Thakkar, Ritesh Soun, Atula Neerkaje, Vasu Sharma, Dipanwita Guhathakurta, and Sudheer Chava. 2022. Tweet Based Reach Aware Temporal Attention Network for NFT Valuation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6321–6332, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.