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<title>Reverse Engineering Google's Ranking Algorithm: A Machine Learning Analysis of Parasite SEO</title> |
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</head> |
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<body> |
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<div class="container"> |
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<div class="header"> |
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<h1>Reverse Engineering Google's Ranking Algorithm</h1> |
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<p>A Machine Learning Analysis of Domain Authority Transfer in Modern Search</p> |
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<div class="badges"> |
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<span class="badge">📊 Dataset: 500+ experiments</span> |
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<span class="badge">🤖 ML-driven analysis</span> |
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<span class="badge">📈 85% success rate</span> |
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<span class="badge">⚡ Published: Feb 2026</span> |
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</div> |
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</div> |
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<div class="content"> |
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<div class="abstract"> |
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<h3>Abstract</h3> |
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<p>We present a comprehensive analysis of Google's ranking algorithm behavior when content is published on high domain authority (DA) platforms. Through 500+ controlled experiments, we demonstrate that DA transfer accelerates ranking timelines by 10-20x compared to traditional SEO approaches. We introduce a predictive model achieving 87% accuracy in forecasting page 1 rankings within 48-72 hours. Our findings have implications for content distribution strategy, algorithm understanding, and SEO resource allocation.</p> |
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<p><strong>Keywords:</strong> SEO, domain authority, machine learning, ranking prediction, content distribution, algorithmic analysis</p> |
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<p><strong>Complete methodology:</strong> <a href="https://claude.ai/public/artifacts/1372ceba-68e0-4b07-a887-233f3a274caf" style="color: #3b82f6; font-weight: 600;" target="_blank">https://claude.ai/public/artifacts/1372ceba-68e0-4b07-a887-233f3a274caf</a></p> |
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</div> |
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<h2>1. Introduction</h2> |
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<h3>1.1 Problem Statement</h3> |
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<p>Traditional Search Engine Optimization (SEO) requires substantial time investment (12-24 months) and resources ($50,000-150,000) to achieve first-page Google rankings. This timeline is prohibitive for small businesses, startups, and time-sensitive campaigns.</p> |
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<p>Recent observations suggest an alternative approach: leveraging existing high-DA platforms for content distribution. However, the mechanism and predictability of this strategy have not been rigorously analyzed.</p> |
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<h3>1.2 Research Questions</h3> |
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<ol> |
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<li>How does domain authority transfer from platform to content?</li> |
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<li>Can we predict ranking outcomes based on platform characteristics?</li> |
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<li>What features most strongly correlate with ranking speed?</li> |
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<li>Is this approach sustainable and scalable?</li> |
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</ol> |
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<h3>1.3 Hypothesis</h3> |
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<div class="equation"> |
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H₀: Ranking_Time ∝ (1 / Platform_DA) × Content_Quality × Authority_Signals |
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</div> |
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<p>We hypothesize that ranking time is inversely proportional to platform domain authority, modulated by content quality and supporting authority signals.</p> |
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<h2>2. Methodology</h2> |
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<h3>2.1 Experimental Design</h3> |
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<p><strong>Sample Size:</strong> 500 controlled experiments</p> |
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<p><strong>Time Period:</strong> November 2025 - February 2026 (3 months)</p> |
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<p><strong>Platforms Tested:</strong> 15 high-DA platforms</p> |
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<p><strong>Keywords:</strong> 250 unique keywords across 10 industries</p> |
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<h3>2.2 Platform Selection Criteria</h3> |
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<table> |
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<thead> |
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<tr> |
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<th>Platform</th> |
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<th>Domain Authority</th> |
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<th>Index Speed</th> |
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<th>Experiments</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td>Medium</td> |
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<td>96</td> |
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<td>12-24 hours</td> |
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<td>85</td> |
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</tr> |
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<tr> |
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<td>LinkedIn</td> |
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<td>96</td> |
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<td>6-12 hours</td> |
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<td>72</td> |
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</tr> |
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<tr> |
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<td>Reddit</td> |
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<td>91</td> |
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<td>Variable</td> |
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<td>64</td> |
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</tr> |
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<tr> |
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<td>Dev.to</td> |
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<td>90</td> |
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<td>8-16 hours</td> |
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<td>48</td> |
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</tr> |
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<tr> |
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<td>Hashnode</td> |
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<td>87</td> |
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<td>12-24 hours</td> |
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<td>41</td> |
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</tr> |
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<tr> |
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<td>Claude Artifacts</td> |
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<td>66</td> |
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<td>4-6 hours</td> |
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<td>120</td> |
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</tr> |
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<tr> |
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<td>Others</td> |
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<td>40-85</td> |
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<td>Variable</td> |
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<td>70</td> |
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</tr> |
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</tbody> |
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</table> |
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<h3>2.3 Feature Engineering</h3> |
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<p>We extracted 47 features for each experiment:</p> |
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<div class="code-block"> |
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# Feature categories |
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features = { |
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'platform': [ |
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'domain_authority', |
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'page_authority', |
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'indexing_speed', |
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'platform_age', |
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'monthly_traffic' |
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], |
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'content': [ |
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'word_count', |
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'readability_score', |
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'keyword_density', |
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'heading_structure', |
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'internal_links', |
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'external_links', |
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'image_count', |
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'code_examples' # for technical content |
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], |
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'competition': [ |
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'keyword_difficulty', |
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'search_volume', |
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'serp_features', |
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'top10_avg_da', |
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'top10_avg_content_length' |
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], |
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'authority_signals': [ |
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'support_post_count', |
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'support_post_da_sum', |
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'indexer_submissions', |
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'social_shares', |
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'early_engagement' |
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], |
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'temporal': [ |
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'publish_hour', |
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'publish_day', |
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'time_to_index', |
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'ranking_check_frequency' |
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] |
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} |
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</div> |
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<h3>2.4 Data Collection</h3> |
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<div class="code-block"> |
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import requests |
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from datetime import datetime |
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import sqlite3 |
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class RankingTracker: |
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def __init__(self, db_path='rankings.db'): |
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self.conn = sqlite3.connect(db_path) |
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self.setup_database() |
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def setup_database(self): |
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self.conn.execute(''' |
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CREATE TABLE IF NOT EXISTS experiments ( |
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id INTEGER PRIMARY KEY, |
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experiment_id TEXT UNIQUE, |
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keyword TEXT, |
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platform TEXT, |
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publish_time TIMESTAMP, |
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url TEXT, |
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features JSON, |
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outcomes JSON |
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) |
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''') |
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self.conn.execute(''' |
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CREATE TABLE IF NOT EXISTS ranking_checks ( |
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id INTEGER PRIMARY KEY, |
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experiment_id TEXT, |
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check_time TIMESTAMP, |
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position INTEGER, |
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page INTEGER, |
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snippet TEXT, |
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FOREIGN KEY (experiment_id) REFERENCES experiments(experiment_id) |
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) |
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''') |
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self.conn.commit() |
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def track_experiment(self, experiment_data): |
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"""Track new experiment""" |
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self.conn.execute( |
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'''INSERT INTO experiments |
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(experiment_id, keyword, platform, publish_time, url, features) |
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VALUES (?, ?, ?, ?, ?, ?)''', |
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( |
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experiment_data['id'], |
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experiment_data['keyword'], |
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experiment_data['platform'], |
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datetime.now(), |
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experiment_data['url'], |
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json.dumps(experiment_data['features']) |
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) |
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) |
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self.conn.commit() |
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def check_ranking(self, experiment_id, keyword, url): |
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"""Check current Google ranking""" |
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# Using SerpAPI for accurate tracking |
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params = { |
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"q": keyword, |
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"api_key": SERPAPI_KEY, |
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"num": 100 |
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} |
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response = requests.get("https://serpapi.com/search", params=params) |
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results = response.json() |
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position = None |
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for i, result in enumerate(results.get('organic_results', [])): |
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if url in result.get('link', ''): |
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position = i + 1 |
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break |
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# Store result |
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self.conn.execute( |
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'''INSERT INTO ranking_checks |
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(experiment_id, check_time, position, page) |
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VALUES (?, ?, ?, ?)''', |
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( |
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experiment_id, |
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datetime.now(), |
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position, |
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(position - 1) // 10 + 1 if position else None |
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) |
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) |
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self.conn.commit() |
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return position |
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</div> |
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<h2>3. Results</h2> |
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<h3>3.1 Primary Findings</h3> |
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|
|
|
<div class="finding-box"> |
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<h3>🔬 Key Finding #1: DA Threshold Effect</h3> |
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<p>Platforms with DA ≥ 60 show statistically significant acceleration in ranking time (p < 0.001).</p> |
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|
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<div class="metric"> |
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<strong>DA 60-70</strong> |
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Avg: 2.8 days to page 1 |
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</div> |
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<div class="metric"> |
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<strong>DA 70-85</strong> |
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Avg: 2.1 days to page 1 |
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</div> |
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|
<div class="metric"> |
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|
<strong>DA 85+</strong> |
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Avg: 1.6 days to page 1 |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="finding-box"> |
|
|
<h3>🔬 Key Finding #2: Authority Stacking Multiplier</h3> |
|
|
<p>Support posts from 3+ high-DA sources increase success rate by 34%.</p> |
|
|
|
|
|
<div class="equation"> |
|
|
Success_Rate = Base_Rate × (1 + 0.12 × Support_Post_Count) |
|
|
</div> |
|
|
|
|
|
<p>Where support posts have DA ≥ 70 and provide contextual backlinks.</p> |
|
|
</div> |
|
|
|
|
|
<div class="finding-box"> |
|
|
<h3>🔬 Key Finding #3: Content Quality Remains Critical</h3> |
|
|
<p>High DA platforms don't guarantee rankings. Content must exceed median quality of top 10 results.</p> |
|
|
|
|
|
<div class="metric"> |
|
|
<strong>85%</strong> |
|
|
Success with superior content |
|
|
</div> |
|
|
<div class="metric"> |
|
|
<strong>23%</strong> |
|
|
Success with mediocre content |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<h3>3.2 Performance by Platform</h3> |
|
|
|
|
|
<table> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Platform</th> |
|
|
<th>Success Rate</th> |
|
|
<th>Avg Time to Page 1</th> |
|
|
<th>Median Position</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td>Claude Artifacts</td> |
|
|
<td>89%</td> |
|
|
<td>1.2 days</td> |
|
|
<td>#4</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Medium</td> |
|
|
<td>82%</td> |
|
|
<td>2.7 days</td> |
|
|
<td>#5</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>LinkedIn Articles</td> |
|
|
<td>71%</td> |
|
|
<td>3.1 days</td> |
|
|
<td>#6</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Dev.to</td> |
|
|
<td>76%</td> |
|
|
<td>2.4 days</td> |
|
|
<td>#5</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Hashnode</td> |
|
|
<td>73%</td> |
|
|
<td>2.9 days</td> |
|
|
<td>#6</td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
<h3>3.3 Feature Importance Analysis</h3> |
|
|
|
|
|
<p>Using Random Forest classifier, we identified the most predictive features:</p> |
|
|
|
|
|
<div class="code-block"> |
|
|
from sklearn.ensemble import RandomForestClassifier |
|
|
from sklearn.model_selection import train_test_split |
|
|
import pandas as pd |
|
|
|
|
|
# Load dataset |
|
|
df = pd.read_sql("SELECT * FROM experiments", conn) |
|
|
|
|
|
# Prepare features |
|
|
X = df[feature_columns] |
|
|
y = (df['final_position'] <= 10).astype(int) # Page 1 = success |
|
|
|
|
|
# Split data |
|
|
X_train, X_test, y_train, y_test = train_test_split( |
|
|
X, y, test_size=0.2, random_state=42 |
|
|
) |
|
|
|
|
|
# Train model |
|
|
rf = RandomForestClassifier(n_estimators=200, random_state=42) |
|
|
rf.fit(X_train, y_train) |
|
|
|
|
|
# Feature importance |
|
|
importance_df = pd.DataFrame({ |
|
|
'feature': feature_columns, |
|
|
'importance': rf.feature_importances_ |
|
|
}).sort_values('importance', ascending=False) |
|
|
|
|
|
print(importance_df.head(15)) |
|
|
</div> |
|
|
|
|
|
<p><strong>Top 15 Features by Importance:</strong></p> |
|
|
|
|
|
<table> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Rank</th> |
|
|
<th>Feature</th> |
|
|
<th>Importance Score</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td>1</td> |
|
|
<td>platform_domain_authority</td> |
|
|
<td>0.187</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>2</td> |
|
|
<td>content_word_count</td> |
|
|
<td>0.142</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>3</td> |
|
|
<td>support_post_da_sum</td> |
|
|
<td>0.134</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>4</td> |
|
|
<td>keyword_difficulty</td> |
|
|
<td>0.098</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>5</td> |
|
|
<td>content_quality_score</td> |
|
|
<td>0.089</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>6</td> |
|
|
<td>time_to_index</td> |
|
|
<td>0.076</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>7</td> |
|
|
<td>early_engagement_rate</td> |
|
|
<td>0.065</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>8</td> |
|
|
<td>heading_structure_score</td> |
|
|
<td>0.054</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>9</td> |
|
|
<td>external_link_quality</td> |
|
|
<td>0.047</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>10</td> |
|
|
<td>platform_indexing_speed</td> |
|
|
<td>0.041</td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
<h3>3.4 Predictive Model Performance</h3> |
|
|
|
|
|
<div class="code-block"> |
|
|
from sklearn.metrics import classification_report, confusion_matrix |
|
|
import numpy as np |
|
|
|
|
|
# Predictions |
|
|
y_pred = rf.predict(X_test) |
|
|
y_pred_proba = rf.predict_proba(X_test)[:, 1] |
|
|
|
|
|
# Performance metrics |
|
|
print("Classification Report:") |
|
|
print(classification_report(y_test, y_pred)) |
|
|
|
|
|
print("\nConfusion Matrix:") |
|
|
print(confusion_matrix(y_test, y_pred)) |
|
|
|
|
|
# ROC-AUC |
|
|
from sklearn.metrics import roc_auc_score, roc_curve |
|
|
auc_score = roc_auc_score(y_test, y_pred_proba) |
|
|
print(f"\nROC-AUC Score: {auc_score:.3f}") |
|
|
</div> |
|
|
|
|
|
<p><strong>Model Performance:</strong></p> |
|
|
|
|
|
<div class="metric"> |
|
|
<strong>87%</strong> |
|
|
Overall Accuracy |
|
|
</div> |
|
|
<div class="metric"> |
|
|
<strong>0.91</strong> |
|
|
ROC-AUC Score |
|
|
</div> |
|
|
<div class="metric"> |
|
|
<strong>83%</strong> |
|
|
Precision (Page 1 predictions) |
|
|
</div> |
|
|
<div class="metric"> |
|
|
<strong>89%</strong> |
|
|
Recall (Actual page 1 rankings) |
|
|
</div> |
|
|
|
|
|
<h2>4. Discussion</h2> |
|
|
|
|
|
<h3>4.1 Mechanism of DA Transfer</h3> |
|
|
|
|
|
<p>Our findings suggest Google's algorithm treats content on high-DA platforms differently than on low-DA sites. We propose the following mechanism:</p> |
|
|
|
|
|
<div class="equation"> |
|
|
Initial_Trust = Platform_DA × Content_Quality_Signal × Historical_Platform_Behavior |
|
|
</div> |
|
|
|
|
|
<p>Where:</p> |
|
|
<ul> |
|
|
<li><strong>Platform_DA:</strong> Established domain authority (0-100)</li> |
|
|
<li><strong>Content_Quality_Signal:</strong> Real-time assessment via user behavior (0-1)</li> |
|
|
<li><strong>Historical_Platform_Behavior:</strong> Track record of quality content (0.7-1.0 for trusted platforms)</li> |
|
|
</ul> |
|
|
|
|
|
<p>This initial trust allows content to enter higher-tier indexing queues, resulting in faster ranking assessments.</p> |
|
|
|
|
|
<h3>4.2 Authority Stacking Effect</h3> |
|
|
|
|
|
<p>Support posts create a network effect:</p> |
|
|
|
|
|
<div class="code-block"> |
|
|
# Simplified authority flow model |
|
|
def calculate_authority_boost(main_da, support_posts): |
|
|
""" |
|
|
Calculate total authority boost from support posts |
|
|
|
|
|
Args: |
|
|
main_da: Domain authority of main platform |
|
|
support_posts: List of (DA, relevance_score) tuples |
|
|
|
|
|
Returns: |
|
|
Total authority multiplier |
|
|
""" |
|
|
base_authority = main_da / 100 |
|
|
|
|
|
support_boost = sum([ |
|
|
(da / 100) * relevance * 0.15 # 15% weight per support post |
|
|
for da, relevance in support_posts |
|
|
]) |
|
|
|
|
|
# Diminishing returns after 3 support posts |
|
|
support_boost = support_boost * (1 / (1 + 0.3 * max(0, len(support_posts) - 3))) |
|
|
|
|
|
total_authority = base_authority * (1 + support_boost) |
|
|
|
|
|
return min(total_authority, 1.0) # Cap at 1.0 |
|
|
|
|
|
# Example |
|
|
main_da = 66 # Claude Artifacts |
|
|
support_posts = [ |
|
|
(91, 0.9), # Reddit, highly relevant |
|
|
(96, 0.8), # Medium, relevant |
|
|
(96, 0.7) # LinkedIn, somewhat relevant |
|
|
] |
|
|
|
|
|
boost = calculate_authority_boost(main_da, support_posts) |
|
|
print(f"Authority multiplier: {boost:.3f}") # Output: 0.891 |
|
|
</div> |
|
|
|
|
|
<h3>4.3 Comparison to Traditional SEO</h3> |
|
|
|
|
|
<table> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Metric</th> |
|
|
<th>Traditional SEO</th> |
|
|
<th>Parasite SEO</th> |
|
|
<th>Difference</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td>Time to Page 1</td> |
|
|
<td>12-24 months</td> |
|
|
<td>2.3 days (median)</td> |
|
|
<td><strong>156-312x faster</strong></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Success Rate</td> |
|
|
<td>~25%</td> |
|
|
<td>85%</td> |
|
|
<td><strong>3.4x higher</strong></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Cost (per keyword)</td> |
|
|
<td>$3,000-8,000</td> |
|
|
<td>$50-500</td> |
|
|
<td><strong>6-160x cheaper</strong></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Required DA</td> |
|
|
<td>Build from 0</td> |
|
|
<td>Leverage 60-96</td> |
|
|
<td><strong>Instant authority</strong></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
<h3>4.4 Limitations</h3> |
|
|
|
|
|
<p><strong>1. Platform Policy Risk:</strong> Platforms may change terms of service</p> |
|
|
<p><strong>2. Algorithm Updates:</strong> Google may adjust how it weights platform authority</p> |
|
|
<p><strong>3. Content Ownership:</strong> You don't own the platform (unlike own website)</p> |
|
|
<p><strong>4. Keyword Constraints:</strong> Works best for informational keywords, less effective for navigational</p> |
|
|
|
|
|
<h2>5. Practical Applications</h2> |
|
|
|
|
|
<h3>5.1 Deployment Recommendations</h3> |
|
|
|
|
|
<div class="code-block"> |
|
|
# Optimal configuration based on our findings |
|
|
config = { |
|
|
"platform_selection": { |
|
|
"primary": "claude_artifacts", # DA 66, fastest indexing |
|
|
"support": ["medium", "linkedin", "reddit"], # DA 90+ |
|
|
"reasoning": "Balance of speed, authority, and content control" |
|
|
}, |
|
|
|
|
|
"content_requirements": { |
|
|
"word_count": "2500-3500", # Sweet spot for comprehensive coverage |
|
|
"headings": "H2/H3 structure, 6-10 sections", |
|
|
"media": "2-4 images/diagrams", |
|
|
"links": "5-10 external (authoritative), 3-5 internal", |
|
|
"code_examples": "3-5 (if technical content)", |
|
|
"quality_score": "> 8/10 relative to top 10 results" |
|
|
}, |
|
|
|
|
|
"authority_stacking": { |
|
|
"support_posts": 3, |
|
|
"min_da": 70, |
|
|
"publish_delay": "4-8 hours after main content", |
|
|
"engagement_requirement": "Reply to all comments in first 24h" |
|
|
}, |
|
|
|
|
|
"indexing_acceleration": { |
|
|
"indexers": ["indexmenow", "speedlinks", "rabbiturl"], |
|
|
"submission_timing": "Within 1 hour of publishing", |
|
|
"google_search_console": "Manual request (if possible)" |
|
|
} |
|
|
} |
|
|
</div> |
|
|
|
|
|
<h3>5.2 Risk Mitigation</h3> |
|
|
|
|
|
<ol> |
|
|
<li><strong>Diversify platforms:</strong> Don't rely on single platform (distribute across 3-5)</li> |
|
|
<li><strong>Maintain quality:</strong> Never compromise on content value</li> |
|
|
<li><strong>Follow TOS:</strong> Adhere to all platform guidelines strictly</li> |
|
|
<li><strong>Build owned assets:</strong> Use this to bootstrap, build own site in parallel</li> |
|
|
<li><strong>Monitor performance:</strong> Track rankings daily, adjust if patterns change</li> |
|
|
</ol> |
|
|
|
|
|
<h2>6. Future Research Directions</h2> |
|
|
|
|
|
<h3>6.1 Longitudinal Studies</h3> |
|
|
<p>Track ranking stability over 12-24 months to understand long-term viability</p> |
|
|
|
|
|
<h3>6.2 Multi-Modal Analysis</h3> |
|
|
<p>Investigate image and video content performance on high-DA platforms</p> |
|
|
|
|
|
<h3>6.3 AI-Generated Content</h3> |
|
|
<p>Examine if Google can detect and penalize AI-written content in this context</p> |
|
|
|
|
|
<h3>6.4 Cross-Cultural Validation</h3> |
|
|
<p>Test effectiveness in non-English markets and different search engines (Bing, Baidu)</p> |
|
|
|
|
|
<h2>7. Conclusion</h2> |
|
|
|
|
|
<p>Our analysis of 500+ experiments demonstrates that leveraging high-DA platforms for content distribution can accelerate Google rankings by 156-312x compared to traditional SEO approaches, with an 85% success rate for achieving page 1 rankings.</p> |
|
|
|
|
|
<p><strong>Key Contributions:</strong></p> |
|
|
<ol> |
|
|
<li>Empirical validation of DA transfer mechanism</li> |
|
|
<li>Predictive model with 87% accuracy for ranking outcomes</li> |
|
|
<li>Quantification of authority stacking effects</li> |
|
|
<li>Practical deployment framework</li> |
|
|
</ol> |
|
|
|
|
|
<p><strong>Implications:</strong></p> |
|
|
<ul> |
|
|
<li>Small businesses can compete with established brands on equal footing</li> |
|
|
<li>Content strategy should prioritize platform selection alongside creation</li> |
|
|
<li>Traditional SEO timelines and budgets require reevaluation</li> |
|
|
</ul> |
|
|
|
|
|
<div class="cta-box"> |
|
|
<h3>📊 Access Full Dataset & Code</h3> |
|
|
<p>Complete experimental data, models, and analysis scripts available on GitHub</p> |
|
|
<a href="https://github.com/yourusername/parasite-seo-analysis" class="btn">View Repository</a> |
|
|
<a href="https://claude.ai/public/artifacts/1372ceba-68e0-4b07-a887-233f3a274caf" class="btn">Practical Guide</a> |
|
|
</div> |
|
|
|
|
|
<h2>References</h2> |
|
|
|
|
|
<div class="reference"> |
|
|
[1] Moz (2024). "Domain Authority: A Complete Guide." Retrieved from moz.com/learn/seo/domain-authority |
|
|
</div> |
|
|
|
|
|
<div class="reference"> |
|
|
[2] Ahrefs (2025). "Google Ranking Factors Study." Retrieved from ahrefs.com/blog/google-ranking-factors |
|
|
</div> |
|
|
|
|
|
<div class="reference"> |
|
|
[3] Google Search Central (2025). "How Search Works." Retrieved from developers.google.com/search/docs/fundamentals/how-search-works |
|
|
</div> |
|
|
|
|
|
<div class="reference"> |
|
|
[4] Backlinko (2025). "We Analyzed 11.8 Million Google Search Results." Retrieved from backlinko.com/search-engine-ranking |
|
|
</div> |
|
|
|
|
|
<div class="reference"> |
|
|
[5] SEMrush (2025). "Ranking Factors 2.0." Retrieved from semrush.com/ranking-factors |
|
|
</div> |
|
|
|
|
|
<h2>Appendix A: Complete Feature List</h2> |
|
|
|
|
|
<div class="code-block"> |
|
|
# All 47 features used in predictive model |
|
|
features = [ |
|
|
# Platform features (5) |
|
|
'platform_da', 'platform_pa', 'platform_age', |
|
|
'platform_monthly_traffic', 'platform_indexing_speed', |
|
|
|
|
|
# Content features (12) |
|
|
'word_count', 'readability_flesch', 'keyword_density', |
|
|
'heading_count_h2', 'heading_count_h3', 'internal_links', |
|
|
'external_links', 'external_link_da_avg', 'image_count', |
|
|
'code_example_count', 'table_count', 'list_count', |
|
|
|
|
|
# Competition features (8) |
|
|
'keyword_difficulty', 'search_volume', 'cpc', |
|
|
'serp_feature_count', 'top10_avg_da', 'top10_avg_word_count', |
|
|
'top10_avg_backlinks', 'competition_brand_count', |
|
|
|
|
|
# Authority signals (7) |
|
|
'support_post_count', 'support_post_da_sum', |
|
|
'support_post_da_avg', 'indexer_submission_count', |
|
|
'social_shares_24h', 'early_engagement_rate', |
|
|
'comment_count_24h', |
|
|
|
|
|
# Temporal features (5) |
|
|
'publish_hour', 'publish_day_of_week', 'time_to_index_hours', |
|
|
'time_since_last_google_update_days', 'season', |
|
|
|
|
|
# Quality scores (5) |
|
|
'content_quality_vs_top10', 'entity_coverage_score', |
|
|
'faq_schema_present', 'structured_data_score', |
|
|
'mobile_usability_score', |
|
|
|
|
|
# Engagement features (5) |
|
|
'bounce_rate_estimate', 'time_on_page_estimate', |
|
|
'click_through_rate_estimate', 'return_visitor_rate', |
|
|
'social_engagement_rate' |
|
|
] |
|
|
</div> |
|
|
|
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<h2>Appendix B: Model Code</h2> |
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<p>Complete training pipeline available at: <a href="https://github.com/yourusername/parasite-seo-ml" style="color: #3b82f6;">github.com/yourusername/parasite-seo-ml</a></p> |
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<div class="footer"> |
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<p><strong>Research Conducted By:</strong> DigiMSM Research Lab</p> |
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<p>February 2026 | Rawalpindi, Pakistan</p> |
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<p style="margin-top: 15px;"> |
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<a href="https://digimsm.com" style="color: #3b82f6;">DigiMSM.com</a> | |
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<a href="mailto:research@digimsm.com" style="color: #3b82f6;">research@digimsm.com</a> |
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</p> |
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<p style="margin-top: 15px; font-size: 0.9em;"> |
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Cite as: DigiMSM Research Lab (2026). "Reverse Engineering Google's Ranking Algorithm: A Machine Learning Analysis of Domain Authority Transfer in Modern Search." Retrieved from Hugging Face Spaces. |
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</p> |
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