Diving Deep: My Social Information Network Project for Spotting Crypto Scams

Diving Deep: My Social Information Network Project for Spotting Crypto Scams

Hey everyone! I'm excited to share a project I've been pouring my heart into for my Social Information Network course. It's all about using the power of social connections and data analysis to try and sniff out one of the biggest problems in the crypto world: rug pulls.

Why Rug Pulls? Why Now?

If you're even remotely involved in cryptocurrency, you've probably heard horror stories of "rug pulls." It's a nasty scam where the creators of a new crypto token hype it up, get a bunch of people to invest, and then suddenly disappear with everyone's money, leaving the token worthless. It's like the digital version of a magician making your money disappear, except there's no cool trick, just deception.

I chose this project because I'm genuinely interested in making the crypto space safer for everyone. I believe that by understanding how these scams operate and by leveraging the power of social information networks, we can build tools to protect ourselves and others.

My Project: Building a Crypto Social Detective

So, what exactly am I building? Think of it as a social detective for the crypto world. It's a system that looks at different kinds of information to try and identify patterns that might indicate a rug pull is in the works.

Here's a breakdown of what my project does:

Blockchain Sleuthing

I'm digging into the actual transaction data on the blockchain. I'm building a "blockchain graph" (build_blockchain_graph). Think of it like a map that shows how money is flowing between different crypto addresses. By analyzing this map, I can identify key players and see how funds are moving around. I'm using metrics like betweenness centrality and eigenvector centrality to find influential accounts.

Social Media Analysis

Scammers often use social media to create hype and lure in investors. So, I'm also analyzing social media data. I'm building a "social graph" (analyze_social_media) that shows how different users are connected and interacting. This involves looking at things like follower counts, engagement, and the content of posts (analyze_content). I use sentiment analysis to gauge the overall feeling around a crypto project.

Meme Coin Mania

Since meme coins are often prime targets for rug pulls, I'm also focusing on analyzing these types of cryptocurrencies. I'm gathering data on various meme coins (fetch_memecoin_data) and building a social network specifically for them (build_social_network). I'm looking at metrics like market cap, trading volume, and even the similarity in names and symbols to identify potential connections and risks.

Combining the Clues

The real magic happens when I combine the blockchain data and social media insights (combine_features). By looking at how activity on the blockchain correlates with social media buzz, I can get a more complete picture of what's going on. Are there a lot of new transactions happening at the same time as a big social media push? That could be a red flag.

Temporal Analysis

This is useful to detect how the social network changes over time (detect_temporal_patterns), if the connections become unstable that means might be a rug pull.

How I Plan to Spot Rug Pulls

My goal is to use all of this information to develop a system that can identify potential rug pulls early on. Here's how I envision it working:

Feature Engineering

Calculate various features based on blockchain data, social media data, and meme coin metrics.

Machine Learning Model Training

Use a machine learning model (train_model), like XGBoost, to learn the patterns that are associated with rug pulls. I'll train the model on historical data of both successful and failed crypto projects.

Risk Scoring

Use the trained model to assign a risk score to new and existing crypto projects. The higher the score, the greater the risk of a rug pull.

Alert System

Develop an alert system that notifies users when a project's risk score crosses a certain threshold.

Project Results & Insights

After running extensive analysis on meme coin networks and their behavior, I've gathered some fascinating insights that demonstrate the effectiveness of our detection system. Let's dive into the key findings:

Network Analysis Results

Analysis revealed key metrics about the meme coin network structure:

Top Influential Coins

Based on degree centrality analysis, these coins showed the highest influence:

Rug Pull Risk Assessment Results

Our system ran tests on various scenarios and produced the following risk assessments:

High Risk Scenario (Risk Score: 0.84)

Medium Risk Scenario (Risk Score: 0.52)

Low Risk Scenario (Risk Score: 0.20)

Key Insights

  1. Community Structure: The network naturally divides into two main communities, with one group centered around established coins (DOGE, SHIB) and another around newer meme coins.

  2. Market Dominance: DOGE and SHIB together control over 78% of the market cap, indicating high market concentration.

  3. Network Resilience: The high average clustering coefficient (0.761) suggests strong interconnectedness, but this could also indicate potential manipulation.

  4. Risk Patterns: High-risk projects typically show a combination of centralized ownership, isolated community structures, and artificial social engagement patterns.

These findings demonstrate the effectiveness of our social network analysis approach in identifying potential rug pulls and understanding the meme coin ecosystem.

Still a Work in Progress...

I want to be upfront: this project is still a work in progress. I'm constantly learning and refining my approach. There are a lot of challenges to overcome, such as:

Data Availability

Getting access to reliable and comprehensive data can be difficult.

Evolving Scams

Scammers are always coming up with new and creative ways to trick people.

False Positives

It's important to minimize the number of false positives (i.e., flagging legitimate projects as potential rug pulls).

Looking Ahead

Despite these challenges, I'm optimistic about the potential of this project. I believe that by combining the power of social information networks with machine learning, we can make a real difference in the fight against crypto scams.

I'm excited to continue working on this project and to share my progress with you all. Stay tuned for future updates!

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