goker/comp-serp-data

Comprehensive SERP Data

This dataset contains comprehensive search engine ranking data collected from Google and Bing, along with extracted technical and content features for analyzing search engine ranking algorithms.

📊 Dataset Overview

• Total Records: 14,465 search results
• Search Engines: Google (5,895 results) and Bing (8,570 results)
• Keywords: 500 diverse search queries
• Features: 20 features including technical scores, content analysis, and ranking metadata

🎯 Research Purpose

This dataset was created to empirically characterize and compare the ranking environments of large-scale search engines (Google and Bing) through systematic data collection and feature extraction. It enables research on:

• Search engine ranking algorithm analysis
• Cross-engine comparison studies
• Technical SEO feature importance
• Content relevance analysis
• Ranking prediction modeling

📁 Dataset Structure

Main Dataset File

• File: datasets/dataset.csv
• Format: CSV (Comma-separated values)
• Encoding: UTF-8
• Size: ~2MB

Column Descriptions

Search Metadata

Column	Type	Description
query	string	The search query used
engine	string	Search engine (google/bing)
position	integer	Ranking position (1-20)
url	string	Full URL of the result
hostname	string	Domain name of the result
file_name	string	Unique identifier for the page

Content Features

Column	Type	Description	Range
query_in_title	integer	Query presence in page title (0/1)	0-1
exact_query_in_title	integer	Exact query match in title (0/1)	0-1
query_in_h1	integer	Query presence in H1 tag (0/1)	0-1
exact_query_in_h1	integer	Exact query match in H1 (0/1)	0-1
query_density_body	float	Query frequency in body content	0.0-1.0
semantic_similarity_title_query	float	Semantic similarity between title and query	0.0-1.0
semantic_similarity_content_query	float	Semantic similarity between content and query	0.0-1.0
word_count	integer	Number of words in page content	0-∞

Technical Features (Lighthouse Scores)

Column	Type	Description	Range
performance_score	float	Page load performance score	0-100
accessibility_score	float	Web accessibility compliance score	0-100
best-practices_score	float	Security and best practices score	0-100
seo_score	float	Search engine optimization score	0-100

Analysis Features

Column	Type	Description
rank_tier	string	Ranking tier (High: 1-5, Medium: 6-10, Low: 11-20)
cluster	integer	K-means cluster assignment (0-5)

🔬 Data Collection Methodology

1. Keyword Selection

• Source: raw/keywords/keywords.csv
• Count: 500 diverse search queries
• Categories: E-commerce, services, information, local search
• Selection Criteria: High search volume, diverse intent types

2. Search Engine Data Collection

• Google: Custom Search API (top 20 results per query)
• Bing: Bing Search API (top 20 results per query)
• Collection Period: Systematic collection with rate limiting
• Error Handling: Retry mechanisms and exception tracking

3. Web Page Processing

• HTML Extraction: Full page content capture using headless browsers
• Screenshot Capture: Visual page representation
• Performance Measurement: Lighthouse scores via PageSpeed Insights API
• Content Analysis: NLP-based feature extraction

4. Feature Extraction

• Technical Features: Automated Lighthouse scoring
• Content Features: Natural language processing and semantic analysis
• Query Matching: Exact and fuzzy matching algorithms
• Semantic Similarity: Sentence transformer-based relevance scoring

📈 Dataset Statistics

Distribution by Search Engine

• Google: 5,895 results (40.7%)
• Bing: 8,570 results (59.3%)

Distribution by Ranking Tier

• High Tier (positions 1-5): 3,784 results (26.2%)
• Medium Tier (positions 6-10): 3,837 results (26.5%)
• Low Tier (positions 11-20): 6,844 results (47.3%)

Cluster Distribution

• Cluster 0: 2,314 results (16.0%)
• Cluster 1: 2,122 results (14.7%)
• Cluster 2: 1,522 results (10.5%)
• Cluster 3: 5,142 results (35.6%)
• Cluster 4: 2,311 results (16.0%)
• Cluster 5: 1,054 results (7.3%)

🛠️ Technical Implementation

Data Collection Pipeline

The dataset was created using the SERP Profiler Kit, a comprehensive research framework with the following components:

1. Data Collection Modules

   • Google Custom Search API integration
   • Bing Search API integration
   • Web scraping with headless browsers
   • PageSpeed Insights API integration

2. Feature Extraction

   • Natural language processing with sentence transformers
   • Technical SEO analysis using Lighthouse
   • Content relevance scoring
   • Query matching algorithms

3. Data Processing

   • Quality validation and outlier detection
   • Feature normalization and standardization
   • Cluster analysis using K-means
   • Statistical analysis and validation

Quality Assurance

• Exception Tracking: Comprehensive error logging
• Data Validation: Multi-stage quality checks
• Outlier Detection: Statistical anomaly identification
• Reproducibility: Deterministic processing with fixed random seeds

📊 Research Applications

1. Clustering Analysis (RQ1)

• Identify distinct ranking profiles using K-means clustering
• Analyze feature patterns across different ranking strategies
• Validate cluster quality using multiple metrics

2. Feature Importance Analysis (RQ2)

• Determine which features most strongly predict ranking positions
• Compare feature importance across ranking tiers
• Identify engine-specific ranking factors

3. Cross-Engine Comparison (RQ3)

• Compare ranking characteristics between Google and Bing
• Analyze feature distribution differences
• Identify engine-specific ranking patterns

4. Ranking Prediction (RQ4)

• Build ordinal logistic regression models for ranking prediction
• Analyze feature coefficients and significance
• Validate model assumptions and performance

🔧 Usage Examples

Python Usage

import pandas as pd

# Load the dataset
df = pd.read_csv('datasets/dataset.csv')

# Basic statistics
print(f"Dataset shape: {df.shape}")
print(f"Engines: {df['engine'].value_counts()}")
print(f"Rank tiers: {df['rank_tier'].value_counts()}")

# Filter by search engine
google_results = df[df['engine'] == 'google']
bing_results = df[df['engine'] == 'bing']

# Analyze technical features
tech_features = ['performance_score', 'accessibility_score', 
                'best-practices_score', 'seo_score']
print(df[tech_features].describe())

# Analyze content features
content_features = ['query_in_title', 'query_in_h1', 
                   'semantic_similarity_title_query', 'word_count']
print(df[content_features].describe())

R Usage

# Load the dataset
df <- read.csv('datasets/dataset.csv')

# Basic analysis
summary(df)
table(df$engine)
table(df$rank_tier)

# Technical features analysis
tech_cols <- c('performance_score', 'accessibility_score', 
               'best.practices_score', 'seo_score')
summary(df[tech_cols])

🔗 Related Resources

• Source Code: SERP Profiler Kit Repository
• Documentation: Complete methodology and implementation details
• Analysis Results: Statistical analysis and visualization outputs

📄 License

This dataset is released under the MIT License. Please ensure compliance with search engine terms of service and website robots.txt files when using this data.

🤝 Contributing

For questions, issues, or contributions:

• Open an issue on the repository
• Review the documentation for methodology details
• Check the analysis results for statistical insights

⚠️ Important Notes

1. API Compliance: This dataset was collected following search engine API terms of service
2. Ethical Scraping: All web scraping was performed with respect to robots.txt files
3. Data Freshness: Search results may change over time; this dataset represents a snapshot
4. Usage Limitations: This dataset is for research purposes only

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Note: This dataset enables systematic analysis of search engine ranking algorithms and provides a foundation for understanding how different factors influence search result positioning across major search engines.