TweetBoost: Influence of Social Media on NFT Valuation

Kapoor, Arnav; Guhathakurta, Dipanwita; Mathur, Mehul; Yadav, Rupanshu; Gupta, Manish; Kumaraguru, Ponnurungam

doi:10.48550/arxiv.2201.08373

Cited by 6 publications

(26 citation statements)

References 13 publications

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“…In the context of NFTs transacions, the study of how different wallets interact was performed in [7] In this study, an attempt was made to identify the wallets that most influence trades using graph-based analysis techniques. Preliminary studies between the relationship between the average price of tokens in an NFT collection and twitter trends was analysed in [17] and [18]. Both works applied learning techniques to predict the future price of assets.…”

Section: State Of Artmentioning

confidence: 99%

The impact of NFT profile pictures within social network communities

Casale-Brunet

Zichichi

Hutchinson

et al. 2022

Proceedings of the 2022 ACM Conference on Information Technology for Social Good

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This paper presents an analysis of the role of social media, specifically Twitter, in the context of non-fungible tokens, better known as NFTs. Such emerging technology framing the creation and exchange of digital object, started years ago with early projects such as "CryptoPunks" and since early 2021, has received an increasing interest by a community of people creating, buying, selling NFTs and by the media reporting to the general public. In this work it is shown how the landscape of one class of projects, specifically those used as social media profile pictures, has become mainstream with leading projects such as "Bored Ape Yacht Club", "Cool Cats" and "Doodles". This work illustrates how heterogeneous data was collected from the Ethereum blockchain and Twitter and then analysed using algorithms and state-of-art metrics related to graphs. The initial results show that from a social network perspective, the collections of most popular NFTs can be considered as a single community around NFTs. Thus, while each project has its own value and volume of exchange, on a social level all of them are primarily influenced by the evolution of values and trades of "Bored Ape Yacht Club" collection.CCS Concepts: • Applied computing → Sociology.

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Section: State Of Artmentioning

confidence: 99%

The impact of NFT profile pictures within social network communities

Casale-Brunet

Zichichi

Hutchinson

et al. 2022

Proceedings of the 2022 ACM Conference on Information Technology for Social Good

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“…They also develop a linear regression model for the price of future primary and secondary sales of NFTs. Kapoor et al 17 further investigate the relationship between social media (in particular Twitter) promotion of an NFT and its sales price and they find that social media features improve the prediction of the valuation. On a tangential direction, Franceschet 18 use the network between sellers (artists) and buyers (collectors) to rate artists and collectors based on network centrality metrics, while also providing investment strategies with different risk/reward profiles based on a variety of network metrics.…”

Section: Background and Related Workmentioning

confidence: 99%

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Pelechrinis¹,

Liu²,

Krishnamurthy³

et al. 2022

Preprint

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Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets -similar to traditional emerging marketplaces -can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE -a random forest model for the conditional density of the dependent variable -to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.

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“…Social media plays an essential role in NFT community development since platforms such as Twitter, Reddit, or Discord become where people know about new events for the NFT projects. Recent works showed that the social media features make improvements for an NFT valuation classification task [16]. Aside from the market, Casale-Brunet et al analyzed the NFT communities on Twitter using social network analysis [4].…”

Section: Nft Market and Social Mediamentioning

confidence: 99%

Understanding NFT Price Moves through Social Media Keywords Analysis

Luo¹,

Jia²,

Li³

2022

Preprint

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Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly. The overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT social media communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for more than half of the 19 top authentic NFT projects but seldom copycat projects. Besides, to investigate the price moves predictability using social media features, we conduct an experiment of predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given social-media-extracted word features and interpret the feature importance to find insights into the NFT communities. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy above the random baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves.

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TweetBoost: Influence of Social Media on NFT Valuation

Cited by 6 publications

References 13 publications

The impact of NFT profile pictures within social network communities

The impact of NFT profile pictures within social network communities

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Understanding NFT Price Moves through Social Media Keywords Analysis

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