| # 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 | |
| ```python | |
| 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 | |
| ```r | |
| # 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](https://github.com/gokerDEV/serp-profiler-kit) | |
| - **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 | |
| --- | |
| **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. | |