Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Table of Links 1 Introduction 2 State of Art 3 Projects Selection and Data Collection 3.1 Blockchain data 3.2 Twitter data 4 Data Analysis and Results 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.3 Hashtags 4.4 The role of the social network community 5 Conclusions and References 5 CONCLUSIONS The NFT phenomenon is a topic that became mainstream during 2021 and has continued to receive increasing attention from early 2022 to the present. This blockchain-based technology has also come to the forefront thanks to projects such as BAYCs whose images have been purchased by celebrities and traded at very high prices as reported by several media. In this paper, we analyzed the phenomenon of NFTs, focusing on the class related to PFPs. We chose 18 projects based on our knowledge in the field and based on their importance in terms of volumes traded, users, and social activity. It was illustrated through analysis of social activity how the concept of social community is essential to ensure liquidity and value of PFP projects, i.e., used as social icons. The centrality and influence of the community related to BAYCs was demonstrated, that despite being second to CryptoPunks in terms of volumes traded, social activity is much higher and central to every other project in the industry. This work aimed to lay the groundwork for more in-depth future studies on understanding whether patterns and causality conditions may exist between elements such as the number of tweets, average price of a collection, Twitter users, and Ethereum wallet. REFERENCES [1] Oluwaseun Ajao, Jun Hong, and Weiru Liu. 2015. A survey of location inference techniques on Twitter. Journal of Information Science 41, 6 (2015), 855–864. https://doi.org/10.1177/0165551515602847 [2] Dune Analytics. 2022. rchen8: OpenSea Dashboard. Retrieved June 6, 2022 from https://dune.xyz/rchen8/opensea [3] Joseph Bonneau, Jonathan Anderson, Ross Anderson, and Frank Stajano. 2009. Eight friends are enough: social graph approximation via public listings. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems. 13–18. https://doi.org/10.1145/1578002.1578005 [4] Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, and Ian J. Taylor. 2019. Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data. In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 717–722. https: //doi.org/10.1109/ICTC46691.2019.8939746 [5] Pinar Caglayan and Zehra Ozkan. 2021. NFTs and copyright: challenges and opportunities. Journal of Intellectual Property Law& Practice 16, 10 (06 2021). https://doi.org/10.1093/jiplp/jpab104 [6] Umit Can and Bilal Alatas. 2019. A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and its Applications 535 (2019), 122372. https://doi.org/10.1016/j.physa.2019.122372 [7] Simone Casale-Brunet, Paolo Ribeca, Patrick Doyle, and Marco Mattavelli. 2021. Networks of Ethereum Non-Fungible Tokens: A Graph-Based Analysis of the ERC-721 Ecosystem. In IEEE Blockchain 2021, 06 - 08 December. https://doi.org/10.1109/Blockchain53845.2021.00033 [8] Joseph Coelho, Dawson D’almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. 2019. Social Media and Forecasting Stock Price Change. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. 195–200. https://doi.org/10.1109/ COMPSAC.2019.10206 [9] CoinMarketCap. 2022. Ethereum historical data. Retrieved June 6, 2022 from https://coinmarketcap.com/currencies/ethereum/historical-data/ [10] Iain J Cruickshank and Kathleen M Carley. 2020. Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering. Applied Network Science 5, 1 (2020), 1–40. https://doi.org/10.1007/s41109-020-00317-8 [11] Roberta De Michele, Stefano Ferretti, and Marco Furini. 2019. On helping broadcasters to promote tv-shows through hashtags. Multimedia Tools and Applications 78, 3 (2019), 3279–3296. https://doi.org/10.1007/s11042-018-6510-7 [12] Decrypt.co. 2022. Hot Ethereum NFT Platform LooksRare Is Rife With Wash Trading - And OK With It. Retrieved June 6, 2022 from https: //decrypt.co/90317/ethereum-nft-market-looksrare-wash-trading [13] Joshua Fair!eld. 2021. Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property. Indiana Law Journal, Forthcoming (04 2021). [14] Stefano Ferretti, Marco Furini, and Manuela Montangero. 2019. Diabetes: What are italian twitter users talking about?. In 2019 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, 1–6. https://doi.org/10.1109/ICCCN.2019.8847039 [15] Anastasia Giachanou and Fabio Crestani. 2016. Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49, 2 (2016), 1–41. https://doi.org/10.1145/2938640 [16] Google. 2022. BigQuery. Retrieved June 6, 2022 from https://cloud.google.com/bigquery/ [17] Shrey Jain, Camille Bruckmann, and Chase McDougall. 2022. NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks. arXiv preprint arXiv:2204.12932 (2022). [18] Arnav Kapoor, Dipanwita Guhathakurta, Mehul Mathur, Rupanshu Yadav, Manish Gupta, and Ponnurungam Kumaraguru. 2022. Tweetboost: Influence of social media on nft valuation. arXiv preprint arXiv:2201.08373 (2022). [19] OpenSea. 2022. OpenSea, the largest NFT marketplace. Retrieved June 6, 2022 from https://opensea.io/ [20] Daniel M Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web. 695–704. https: //doi.org/10.1145/1963405.1963503 [21] İlker Türker and Eyüb Ekmel Sulak. 2018. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links. International Journal of Modern Physics B 32, 04 (2018), 1850029. https://doi.org/10.1142/S0217979218500297 [22] Twitter. 2022. Search Tweets API. Retrieved June 6, 2022 from https://developer.twitter.com/en/docs/twitter-api/tweets/search/introduction [23] Kishore Vasan, Milán Janosov, and Albert-László Barabási. 2022. Quantifying NFT-driven networks in crypto art. Scienti!c reports 12, 1 (2022), 1–11. [24] Shermin Voshmgir. 2020. Token Economy: How the Web3 reinvents the Internet. Vol. 2. Token Kitchen. [25] Andi Wiliam, Sasmoko Sasmoko, Wibowo Kosasih, and Yasinta Indrianti. 2020. Sentiment analysis of social media engagement to purchasing intention. In Understanding Digital Industry. Routledge, 362–365. https://doi.org/10.1201/9780367814557-86 [26] Nick Yee, Jeremy N. Bailenson, and Nicolas Ducheneaut. 2009. The Proteus Effect: Implications of Transformed Digital Self Representation on Online and O&ine Behavior. Communication Research 36, 2 (2009), 285–312. https://doi.org/10.1177/0093650208330254 arXiv:https://doi.org/10.1177/0093650208330254 This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Authors: Authors: (1) SIMONE CASALE-BRUNET, École Polytechnique Fédérale de Lausanne, Switzerland; (2) MIRKO ZICHICHI, Universidad Politécnica de Madrid, Spain; (3) LEE HUTCHINSON, WhaleAnalytica.com, Switzerland; (4) MARCO MATTAVELLI, École Polytechnique Fédérale de Lausanne, Switzerland; (5) STEFANO FERRETTI, University of Urbino “Carlo Bo”, Italy. Table of Links 1 Introduction 1 Introduction 2 State of Art 2 State of Art 3 Projects Selection and Data Collection 3 Projects Selection and Data Collection 3.1 Blockchain data 3.1 Blockchain data 3.2 Twitter data 3.2 Twitter data 4 Data Analysis and Results 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.1 Ethereum wallets and Twitter users and 4.2 The communities 4.3 Hashtags 4.3 Hashtags 4.4 The role of the social network community 4.4 The role of the social network community 5 Conclusions and References 5 Conclusions and References 5 CONCLUSIONS The NFT phenomenon is a topic that became mainstream during 2021 and has continued to receive increasing attention from early 2022 to the present. This blockchain-based technology has also come to the forefront thanks to projects such as BAYCs whose images have been purchased by celebrities and traded at very high prices as reported by several media. In this paper, we analyzed the phenomenon of NFTs, focusing on the class related to PFPs. We chose 18 projects based on our knowledge in the field and based on their importance in terms of volumes traded, users, and social activity. It was illustrated through analysis of social activity how the concept of social community is essential to ensure liquidity and value of PFP projects, i.e., used as social icons. The centrality and influence of the community related to BAYCs was demonstrated, that despite being second to CryptoPunks in terms of volumes traded, social activity is much higher and central to every other project in the industry. This work aimed to lay the groundwork for more in-depth future studies on understanding whether patterns and causality conditions may exist between elements such as the number of tweets, average price of a collection, Twitter users, and Ethereum wallet. REFERENCES [1] Oluwaseun Ajao, Jun Hong, and Weiru Liu. 2015. A survey of location inference techniques on Twitter. Journal of Information Science 41, 6 (2015), 855–864. https://doi.org/10.1177/0165551515602847 [2] Dune Analytics. 2022. rchen8: OpenSea Dashboard. Retrieved June 6, 2022 from https://dune.xyz/rchen8/opensea [3] Joseph Bonneau, Jonathan Anderson, Ross Anderson, and Frank Stajano. 2009. Eight friends are enough: social graph approximation via public listings. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems. 13–18. https://doi.org/10.1145/1578002.1578005 [4] Evan Brinckman, Andrey Kuehlkamp, Jarek Nabrzyski, and Ian J. Taylor. 2019. Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data. In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 717–722. https: //doi.org/10.1109/ICTC46691.2019.8939746 [5] Pinar Caglayan and Zehra Ozkan. 2021. NFTs and copyright: challenges and opportunities. Journal of Intellectual Property Law& Practice 16, 10 (06 2021). https://doi.org/10.1093/jiplp/jpab104 [6] Umit Can and Bilal Alatas. 2019. A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and its Applications 535 (2019), 122372. https://doi.org/10.1016/j.physa.2019.122372 [7] Simone Casale-Brunet, Paolo Ribeca, Patrick Doyle, and Marco Mattavelli. 2021. Networks of Ethereum Non-Fungible Tokens: A Graph-Based Analysis of the ERC-721 Ecosystem. In IEEE Blockchain 2021, 06 - 08 December. https://doi.org/10.1109/Blockchain53845.2021.00033 [8] Joseph Coelho, Dawson D’almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. 2019. Social Media and Forecasting Stock Price Change. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. 195–200. https://doi.org/10.1109/ COMPSAC.2019.10206 [9] CoinMarketCap. 2022. Ethereum historical data. Retrieved June 6, 2022 from https://coinmarketcap.com/currencies/ethereum/historical-data/ [10] Iain J Cruickshank and Kathleen M Carley. 2020. Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering. Applied Network Science 5, 1 (2020), 1–40. https://doi.org/10.1007/s41109-020-00317-8 [11] Roberta De Michele, Stefano Ferretti, and Marco Furini. 2019. On helping broadcasters to promote tv-shows through hashtags. Multimedia Tools and Applications 78, 3 (2019), 3279–3296. https://doi.org/10.1007/s11042-018-6510-7 [12] Decrypt.co. 2022. Hot Ethereum NFT Platform LooksRare Is Rife With Wash Trading - And OK With It. Retrieved June 6, 2022 from https: //decrypt.co/90317/ethereum-nft-market-looksrare-wash-trading [13] Joshua Fair!eld. 2021. Tokenized: The Law of Non-Fungible Tokens and Unique Digital Property. Indiana Law Journal, Forthcoming (04 2021). [14] Stefano Ferretti, Marco Furini, and Manuela Montangero. 2019. Diabetes: What are italian twitter users talking about?. In 2019 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, 1–6. https://doi.org/10.1109/ICCCN.2019.8847039 [15] Anastasia Giachanou and Fabio Crestani. 2016. Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49, 2 (2016), 1–41. https://doi.org/10.1145/2938640 [16] Google. 2022. BigQuery. Retrieved June 6, 2022 from https://cloud.google.com/bigquery/ [17] Shrey Jain, Camille Bruckmann, and Chase McDougall. 2022. NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks. arXiv preprint arXiv:2204.12932 (2022). [18] Arnav Kapoor, Dipanwita Guhathakurta, Mehul Mathur, Rupanshu Yadav, Manish Gupta, and Ponnurungam Kumaraguru. 2022. Tweetboost: Influence of social media on nft valuation. arXiv preprint arXiv:2201.08373 (2022). [19] OpenSea. 2022. OpenSea, the largest NFT marketplace. Retrieved June 6, 2022 from https://opensea.io/ [20] Daniel M Romero, Brendan Meeder, and Jon Kleinberg. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web. 695–704. https: //doi.org/10.1145/1963405.1963503 [21] İlker Türker and Eyüb Ekmel Sulak. 2018. A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links. International Journal of Modern Physics B 32, 04 (2018), 1850029. https://doi.org/10.1142/S0217979218500297 [22] Twitter. 2022. Search Tweets API. Retrieved June 6, 2022 from https://developer.twitter.com/en/docs/twitter-api/tweets/search/introduction [23] Kishore Vasan, Milán Janosov, and Albert-László Barabási. 2022. Quantifying NFT-driven networks in crypto art. Scienti!c reports 12, 1 (2022), 1–11. [24] Shermin Voshmgir. 2020. Token Economy: How the Web3 reinvents the Internet. Vol. 2. Token Kitchen. [25] Andi Wiliam, Sasmoko Sasmoko, Wibowo Kosasih, and Yasinta Indrianti. 2020. Sentiment analysis of social media engagement to purchasing intention. In Understanding Digital Industry. Routledge, 362–365. https://doi.org/10.1201/9780367814557-86 [26] Nick Yee, Jeremy N. Bailenson, and Nicolas Ducheneaut. 2009. The Proteus Effect: Implications of Transformed Digital Self Representation on Online and O&ine Behavior. Communication Research 36, 2 (2009), 285–312. https://doi.org/10.1177/0093650208330254 arXiv:https://doi.org/10.1177/0093650208330254 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv
