Part 1 : This is what happens when you don’t care about your crypto privacy on Twitter (Python)
About 2 months ago, a skilled data specialist created a database containing 117,394 Twitter accounts associated with crypto addresses. These addresses, along with the corresponding Twitter accounts and ENS addresses, were made available in a public Google Drive database.
Part 1: How did he do it ?
If we have a look at the dataset, users were filtered by names ending with “.eth”. Those are ENS (Ethereum Name Service) domains. An ENS, or Ethereum Name Service, is a decentralized system for resolving human-readable names to machine-readable addresses on the Ethereum blockchain. It allows users to send transactions and access decentralized applications using easily remembered names, instead of long and complex address strings. They are important as they directly link up to their associated Ethereum account.
Below, I’ve created a quick and dirty python example on how to get a json array with Twitter user’s name ending with .eth, as well as their name, displayed name, id, followers, verified status.
Once you have the list of ENS domains from Twitter, you can get the associated Ethereum public address using the web3.py library (as shown below). Web3.py is a Python library for working with the Ethereum blockchain and its associated technologies, such as smart contracts. It provides a high-level interface for interacting with the Ethereum blockchain and allows developers to write Python code to build decentralized applications (dapps) on the Ethereum platform.
In conclusion, the data scientist was able to generate the file linking Twitter accounts with their respective public Ethereum addresses through technical expertise. It is likely that he had access to a premium account on Twitter, which allowed him to retrieve more than 100,000 addresses, providing a rich dataset for further analysis.
This concludes Part 1
The widespread use of social media has revolutionized the way we communicate and share information, but it has also introduced significant risks to personal privacy. When we use social media platforms like Twitter, we are constantly generating vast amounts of personal data that can be easily accessed and analyzed by others. This data can include our thoughts, opinions, and personal preferences, as well as our physical locations, connections to other users, and even our financial situation.
In part 2, we will focus on the financial information that can be gleaned from these addresses. When combined, this data can create a detailed picture of our personal lives and habits. And it is important to recognize the potential for misuse by hackers, scammers, and other malicious actors who may use this information to steal our identities, defraud us, or even cause us physical harm. It is crucial to be cautious and protect ourselves against these threats.
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