One of the Most Important Algorithmic Authorship Rules I Follow While Auditing Content 👇

It’s all about matching the tenses and modality of the answer with the question asked in the content.

🧐 Why is this important? If you've been following me and Koray Tugberk GÜBÜR, you probably understand the importance of using question formats for headings in content.

📜 Did you know? There’s a Google patent on how it generates related questions for search queries. (Link in comments!)

✅ The SEO Benefit – When questions are structured properly within a semantic content network, they can help boost your content’s Unique Information Gain Score, increasing its authority and relevance.

🔹 Matching Tense & Modality: The Golden Rule

• 📌 If a question is in Present Indefinite tense, the answer should also be in Present Indefinite tense.
• 📌 If a question contains a modal verb (e.g., can, may, will, would, could), the answer should contain the same modal verb.

Example:

• ❌ Q: How could this strategy improve SEO?
  ❌ A: This strategy could improve SEO by. (Using "could" in both)

🔮 Why does this matter? Small optimizations like these enhance semantic alignment and content structure, amplifying the positive compound effect across your entire website.

🚀 Implement this, and watch your SEO performance grow exponentially!

Q/A Examples

1️⃣ Present Indefinite Tense

• ✅ Q: How does internal linking impact SEO?
  ✅ A: Internal linking improves SEO by enhancing crawlability and distributing link equity.
• ❌ A: Internal linking has improved SEO by enhancing crawlability. (Incorrect tense)

2️⃣ Past Indefinite Tense

• ✅ Q: How did Google update its ranking algorithm in 2023?
  ✅ A: Google updated its ranking algorithm in 2023 by incorporating AI-driven search enhancements.
• ❌ A: Google updates its ranking algorithm. (Incorrect tense)

3️⃣ Future Tense

• ✅ Q: How will AI shape the future of content marketing?
  ✅ A: AI will shape the future of content marketing by generating more personalized experiences.
• ❌ A: AI shapes the future of content marketing. (Mismatch in tense)

4️⃣ Matching Modal Verbs

• ✅ Q: How can structured data improve search rankings?
  ✅ A: Structured data can improve search rankings by helping search engines understand content better.
• ❌ A: Structured data improves search rankings. (Modal verb "can" is missing)
• ✅ Q: What would happen if a website ignored technical SEO?
  ✅ A: If a website ignored technical SEO, it would struggle to rank effectively in search results.
• ❌ A: If a website ignored technical SEO, it will struggle to rank. (Mismatch in modality)

🌌 Reference from Google Patent No: US9213748B1

📌 FIG. 3: How the System Finds Related Questions

This process helps find related questions based on a user's search query.

🔍 Receiving the Search Query (Step 310)

• The system gets a search query from the user.

🔎 Finding Search Results (Step 320)

• The search engine looks for relevant results.
• Each result is given a score based on relevance.

🏆 Choosing the Best Results (Step 330)

• The system picks the top-rated search results.

📂 Grouping Topics (Step 340)

• It creates topic sets from past user queries that led to the same results.

🔗 Finding Related Questions (Step 350)

• The system selects related questions based on these topic sets.
• Questions are ranked by search frequency.
• Factors that improve ranking:
  • ✅ If the question has an answer.
  • ✅ Quality of the answer (length, relevance, source credibility).
  • ✅ How many users have searched for it.

📲 Showing Related Questions (Step 360)

• The system sends the related questions to the user's device.

📌 FIG. 4: How the System Groups Topics for Search Results

This process organizes topic sets for search results.

🔍 Finding Relevant Search Queries (Step 410)

• Queries are qualified if users clicked or selected a result after searching.
• Selections can be made by clicking, voice commands, or touch.

📊 Ranking the Queries (Step 420)

• Queries are ranked based on search frequency and user selection.

📌 Choosing Topic Sets (Step 430)

• The system picks the top-ranked queries as topic sets.
• Low-ranking topics are removed.

📌 FIG. 5: Finding Related Questions Using Topic Sets

This process finds and ranks related questions using topic sets.

🔍 Finding Matching Questions (Step 510)

• The system looks for questions that contain topic set words.
• It may also generate new questions using templates (e.g., “What is the cure for [X]?”).

📊 Ranking Questions (Step 520)

• Questions are ranked based on search frequency.

❌ Removing Duplicate Questions (Step 530)

• The system removes duplicate questions by checking if they have the same meaning.
• If two questions are the same, the less popular one is removed.

🏆 Selecting the Best Questions (Step 540)

• The highest-ranked questions are shown to the user.

📌 FIG. 6: Creating a Question Graph

This process helps organize and remove duplicate questions.

📌 Creating Question Nodes

• Each unique question is represented as a node in a graph.

🔗 Finding Equivalent Questions

• Questions that mean the same thing are connected.

❌ Removing Duplicates

• If two questions are the same, the less popular one is removed.

🏆 Choosing the Best Version

• The system picks the best version of a question based on:
• ✅ Search frequency.
• ✅ Predefined question formats.