Authors:
(1) Simone Casale-BRUNET, école polytechnique fédérale de lausanne, Switzerland;
(2) Merko Zishichi, University of Politkantica de Madrid, Spain;
(3) Lee Hutchinson, Whaleanalytica.com, Switzerland;
(4) Marco Mattavelli, école polytechnique fédérale de lausanne, Switzerland;
(5) Stefano Ferretti, Urbino University “Carlo Bou”, Italy.
Links table
1 introduction
2 technical cases
3 Projects selection and collection of data
3.1 Blockchain data
3.2 Twitter data
4 data analysis and results
4.1 Ethereum Governor, Twitter users and 4.2 societies
4.3 Retail brands
4.4 The role of the social network community
5 conclusions and references
As another analysis point, we focused on retail marks and how to use them in tweets. Retail marks – which are the words preceded by the symbol # like # NFT – are actually the most common way that Twitter users use to create
Specific groups and contacts. We identified 60,060 different retailers, and therefore we have built an uncomfortable graphic fee as: each knot represents a classification mark and every connection between the two contracts represents the existence of a tweet in which both corresponding hashtin were used. Since the weight of each knot was set the number of tweets in which the corresponding retail brand was set, and since the weight of each connection, the number of tweets containing the joint opposite retail marks. Fig. After twenty, that is, art, weights are much lower. In general, the characteristics of the chart that were formed in Table 3 A: The graph consists of 762,950 connections, with an average weight of about 29,634 common tweets. The average assembly coefficient and the relatively important number of connected components (i.e. 439) indicates the presence of highly specialized societies in this content graph. However, the density of the edge is very low in this case, as the retailers are mainly related to some central retail marks (see below). Most likely for this, the typical has a low value. while
Fig. Again, the values that exceed 20 (Famorie, related to Cryptomories) and the weight are much lower. Figure 4 B shows that the graph taking into account only these twenty brands, as the size of the contract is suitable for Pagerank: again, as it can be noticed, the NFT classification marks, Cryptopunks and Bayc are the most in “UALD. Everything.
Figure 7 shows the trends in size, average price, tweets, and users (conservative and Twitter accounts) for a number of very different projects from the date of construction until April 14, 2022. We can see, Bayc built a continuous participation on Twitter, where the number of unique users who talks about them has grown, as did the average price, which moved from 0.08 to more than 1008 in less than a year. There are projects, such as Cryptohodles, on the other hand, as although creators are trying to build an ecosystem around their project by distributing comedy or new reserved NFT development, society has not grown, which affects the average price and liquidity (in terms of daily deals) for this project. Mekaverse and MFERS, summarized these concepts: For the first time, we see how a large number of tweets in the early stages of the project led to an initial initial value where statues were circulated at the lowest price of 8th, then went to 10 times with a decrease in the number of tweets. For the latter, on the other hand, we see that participation in terms of daily tweets coincides with the price increased, and this happens gradually and continuously after his release.
