nahiar/facebook_spam_detection

Facebook Spam Detection Dataset

Dataset Summary

This dataset contains 600 Facebook profiles with behavioral and activity features designed for spam detection in social media. The dataset enables binary classification to distinguish between spam accounts (Label=1) and legitimate accounts (Label=0), providing insights into spammer behavior patterns on Facebook.

Dataset Details

• —Total Samples: 600 profiles
• —Classes: Binary (0 = Legitimate, 1 = Spam)
• —Class Distribution: Imbalanced (17.2% spam, 82.8% legitimate)
• —Features: 14 behavioral characteristics + 1 target label
• —Format: CSV file

Features Description

Key Statistics

• —Network Size: Average 1,066 friends and 1,069 following
• —Community Engagement: Average 208 communities joined
• —Account Maturity: Average age of 1,215 days (~3.3 years)
• —Content Activity:
• —Average 1,158 posts shared
• —Average 370 URLs shared
• —Average 1,121 photos/videos posted
• —Engagement Metrics:
• —Average 1.6 comments per post
• —Average 0.88 likes per post
• —Average 16 tags per post

Class Imbalance

⚠️ Important: This dataset is imbalanced:

• —Legitimate accounts: 497 samples (82.8%)
• —Spam accounts: 103 samples (17.2%)

Consider using techniques like SMOTE, class weighting, or balanced sampling for training.

Use Cases

This dataset is ideal for:

• —Spam Detection: Build classifiers to identify Facebook spam accounts
• —Behavioral Analysis: Study differences between spam and legitimate user behavior
• —Anomaly Detection: Develop unsupervised methods for suspicious activity detection
• —Social Media Security: Research automated content moderation systems
• —Imbalanced Learning: Practice techniques for handling skewed datasets

Quick Start

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from imblearn.over_sampling import SMOTE

# Load dataset
df = pd.read_csv('Facebook Spam Dataset.csv')

# Prepare features and target
X = df.drop(['Label', 'profile id'], axis=1)
y = df['Label']

# Handle class imbalance with SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X_resampled, y_resampled, test_size=0.2, random_state=42, stratify=y_resampled
)

# Train model
model = RandomForestClassifier(
    n_estimators=100, 
    class_weight='balanced',
    random_state=42
)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print("Classification Report:")
print(classification_report(y_test, y_pred))

Suggested Approaches

Traditional ML

• —Random Forest: Handles mixed data types well
• —Gradient Boosting: XGBoost, LightGBM for performance
• —SVM: With RBF kernel for non-linear patterns
• —Logistic Regression: With proper feature scaling

Handling Imbalance

• —Sampling: SMOTE, ADASYN for oversampling
• —Cost-sensitive: Class weights in algorithms
• —Ensemble: Balanced bagging, EasyEnsemble
• —Metrics: Focus on F1-score, AUC-ROC, precision/recall

Feature Engineering

• —Ratios: Create engagement ratios (likes/posts, comments/posts)
• —Behavioral: URL sharing patterns, media content ratios
• —Network: Friend-to-following ratios, community participation
• —Temporal: Account age interactions with activity levels

Model Evaluation Tips

Given the class imbalance, prioritize these metrics:

• —F1-Score: Harmonic mean of precision and recall
• —AUC-ROC: Area under the ROC curve
• —Precision/Recall: Especially for spam class (minority)
• —Confusion Matrix: To understand false positives/negatives

Data Quality

• —✅ Complete Data: No missing values
• —⚠️ Class Imbalance: 82.8% legitimate vs 17.2% spam
• —✅ Feature Variety: Network, content, and engagement metrics
• —✅ Realistic Ranges: All features show plausible Facebook activity patterns

Research Opportunities

1. 1.Behavioral Patterns: What distinguishes spam from legitimate user behavior?
2. 2.Feature Importance: Which metrics are most predictive of spam accounts?
3. 3.Temporal Analysis: How does account age correlate with spam likelihood?
4. 4.Network Effects: Do spam accounts show distinct networking patterns?
5. 5.Content Analysis: How do URL sharing and media patterns differ?

Potential Applications

• —Social Media Platforms: Automated spam account detection
• —Content Moderation: Flagging suspicious posting patterns
• —User Safety: Protecting users from spam and malicious content
• —Research: Understanding social media abuse patterns
• —Security Systems: Real-time threat detection algorithms

Citation

@dataset{facebook_spam_detection_2024,
  title={Facebook Spam Detection Dataset},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/nahiar/facebook-spam-detection}
}

Notes

• —The age feature appears to be in days rather than years
• —Some ratio features (like fpurls, fpphotos/videos) may exceed 1.0, indicating normalized metrics
• —Consider feature scaling for distance-based algorithms
• —The dataset reflects Facebook's ecosystem and user behavior patterns