Datasets:

nahiar
/

facebook_spam_detection

license: mit
task_categories:
  - tabular-classification
language:
  - en
tags:
  - social-media
  - spam-detection
  - facebook
  - cybersecurity
  - machine-learning
  - binary-classification
  - fraud-detection
size_categories:
  - n<1K

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

Feature	Type	Description	Range
`profile id`	Integer	Unique profile identifier	1-600
`#friends`	Integer	Number of friends	4-5,554
`#following`	Integer	Number of accounts being followed	1-5,312
`#community`	Integer	Number of communities/groups joined	12-1,789
`age`	Integer	Account age (likely in days)	125-2,697
`#postshared`	Integer	Total number of posts shared	76-3,896
`#urlshared`	Integer	Number of URLs shared in posts	11-2,956
`#photos/videos`	Integer	Number of photos/videos posted	65-3,891
`fpurls`	Float	Frequency/proportion of URLs in posts	0.01-1.09
`fpphotos/videos`	Float	Frequency/proportion of media content	0.0-2.74
`avgcomment/post`	Float	Average comments per post	0.0-665
`likes/post`	Float	Average likes per post	0.1-2.8
`tags/post`	Integer	Tags used in posts	10-99
`#tags/post`	Integer	Number of tags per post	1-32
`Label`	Integer	Target variable - Spam (1) or Legitimate (0)	0-1

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

Behavioral Patterns: What distinguishes spam from legitimate user behavior?
Feature Importance: Which metrics are most predictive of spam accounts?
Temporal Analysis: How does account age correlate with spam likelihood?
Network Effects: Do spam accounts show distinct networking patterns?
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