20,000 places under the data – A rather new work in what we are used to seeing in the cryptosphere has aroused our curiosity. We therefore saw fit to share the ins and outs with the company of its director. Thus, present on twitter under the pseudonym “Au Coin Du Cercle”, the data analysis enthusiast at the origin of this map, shared with us his knowledge and clarifications on the subject. If social mapping may seem like an unimportant field, the reality is quite different. It is thus in particular thanks to these tools that we can perceive the extent of the power of our data, here however basic and accessible to all. Tools of course used by almost all the companies to which we deliver our “private” life on a silver platter. In the continuity of the subject, it seemed obvious to make the link between social network and the blockchain.
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Interview with the creator of the Au Coin du Cercle account: mapping the trend
Can you make a short presentation of your background and your professional activity? A link with data analysis?
So no, nothing to do with data analysis. I am 19 years old, so not a lot of professional activity at the moment. I had a classic course, general high school and BAC S then an engineering preparation course which I didn’t like, so I’m waiting to go back to school for next year. Since mid-February I put myself fully into computer development, which I have been doing for 3 years as an autodidact.
On twitter you have recently shared a very interesting social mapping work representing a significant part of French-speaking crypto twitter, could you explain in broad outline what it consists of?
In fact it’s an algorithm that will collect all the people subscribed to my feed and from there it forms what is called in statistics a population. With this population the algorithm will make the links between the accounts, for example if A and B follow me and they also follow each other then a link will be established. Thanks to that, we’re going to see communities forming, if people follow each other it’s not by chance, it’s because their content is close in some way.
In this card, the bigger the account, the wider its circle. Colors represent different types of content.
How do you go about making a map of this scale and how much time do you need?
In fact time is not an issue in absolute terms, the algorithm could do it in 2 minutes. But unfortunately I am restricted by the twitter api which allows to have the information of only one account per minute. For 800 people it actually becomes long.
As you stated yourself when it was published, the purpose of this mapping is to understand the communities and interactions within a group, but what can this information be used for concretely?
Personally, I have already used it to find out how certain communities work. In the crypto twitter there was in particular a community which was really shady, particularly in terms of scams. So I used this tool to see the interactions, if it’s a very open community it will show on the graph because it would have a lot of links with other clusters (subset). And for example if we had to represent a sect we would clearly see 2 very distinct blocks, on one side the classic space and on the other the sect with little opening on the rest. So it really helped me a lot in my research.
Could this information be of interest to companies?
Yes, it is an extremely powerful tool for targeting. Thanks to this, we can, among other things, know who his audience is. I have the example of a friend who was looking to improve her editorial line because she didn’t really have the community she would have liked to have. By identifying certain clusters she could direct her content towards those most likely to be interested, by interacting more with a particular cluster she gains followers from this cluster.
In addition to the idea of the editorial line to be adopted, companies can also have the representation of a particular market.
Does a realization as it stands highlight a particular phenomenon?
Yes we can see some things like the conversion of subscribers for example. On the map, we notice that the account below attracts a large audience that is not sensitive to the general content of the map but that it has most likely converted part of its audience to this content.
What does it tell you about people and their behavior, let’s take your map as an example?
Above all, it helps to understand the impact of tweets. If these 3 green accounts share the same content then they will have almost as much weight as this purple account because they are the 3 lead points of the green community.
![Mapping crypto accounts on Twitter](https://journalducoin-com.exactdn.com/app/uploads/2022/03/zoom-map-modif-3.jpg?strip=all&lossy=1&quality=66&resize=1500%2C945&ssl=1)
In this case the majority of the green community will see their content. So we know that the information will spread.
Does this skill serve you in the crypto industry? Whether for a personal or professional reason?
Yes indirectly in the sense of whether or not it does not currently bring me money but it allows me to avoid certain content.
We see how the result is relevant and only with basic data, is it possible to draw a portrait at the limit of reality if we start to add more subtle data?
Yes, we can give an extremely precise profile on a personality, in particular on his passions and his centers of interest.
Speaking of more subtle data, so you are in the field of OSINT, could you summarize in a few words what it represents and what are the main goals?
OSINT is open source technical intelligence, it is intelligence methods using free data. Basically, it’s amateur intelligence, using only free access data and no hacking. The data that I used for my map are, for example, accessible to everyone. Basically we only use the submerged part of the iceberg. It’s kind of very advanced journalism.
Thanks to the OSINT we can monitor a lot of things and in particular people, wouldn’t a holder of cryptocurrencies be an easier target due to the activities of his wallet if he were to be identified?
Yes. I noticed that there was precisely an incredible amateurism in the sense that twittos choose not to share their information and to remain pseudonymous (they do not openly share their name, wallet address, etc.). But have big gaps regarding their personal security with indirect data sharing. Typically the example I see the most is the NFT shared on twitter for flex. For me when an influencer shared an NFT it was obvious that he was using a wallet dedicated to NFTs, it seemed unimaginable to me whether it was his main or secondary wallet. And yet this is what happens in the vast majority of cases. The advantage is that we can quickly unmask pseudo-whales who earn more in partnership than in crypto.
Do you actually use this cryptographic activity feed during your investigations?
Yes clearly. It’s ideal because it’s immutable, everyone knows it blockchain is the worst place to lie. Lying on the blockchain amounts to writing “yes I lied” all accompanied by a signature. The evidence will never be erased.
Besides, is it that difficult to identify a person to his crypto wallet? What information is sufficient to make the link?
Generally one info is not enough but any info is good to take. You need to have a bridge that links the social network and the blockchain. This bridge can be anything, a transaction that so and so has shared or as said before an NFT. Many times people don’t bother to leave no traces, even for rugs which are in most cases complete amateurism.
To conclude, if you had one piece of advice to give to someone tired of the transparency of their personal information, including crypto txs in particular, what would it be?
First it would be containerize your information, for example using a different nickname for each social network. Let’s imagine that my nickname is “lambda”, just adding a number or modifying a letter prevents search algorithms from making the link. The idea is really to differentiate his Opensea account from his twitter just like the rest.
And secondly, the most important thing is to understand what you are doing. You should know that each interaction is traceable, to avoid this it is very practical to use tornado cash for example. Tornado cash breaks any link between 2 wallets.
I really encourage people to be in control of their data, each information “given” to the internet should lead to a risk/benefit reflection in my opinion.
Data analysis is a fascinating field and here we have a very telling use case that Au Coin du Cercle was kind enough to detail.
As exciting as the field is, its usefulness is also cause for concern because of the disconcerting efficiency with which our data is collected. The very idea that our actions and gestures are interpreted for commercial and political ends is dizzying. The problem extends of course to real life, but it is on the internet that it has the most weight. Taking the blue pill, however, is not necessarily problematic according to our vision of things as long as we do it in full awareness of the consequences of our choices. Unfortunately the problem is not new, even if Web 3 still brings hope of a (partial) recovery of control of our data.
However, maintaining digital hygiene remains essential because the blockchain can prove to be the major asset of GAFAM. Our digital identity is precious, let’s avoid offering it to the first comer.
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