Geo Brand Mentions

Based on the Ahrefs December 2025 study and corroborating research from Profound (2025) and Terakeet (2025):

1. YouTube Mentions -- Correlation ~0.737 (STRONGEST)

Why YouTube matters most:

• YouTube is the second-largest search engine and the largest video platform globally (2.5B+ monthly users).
• AI training datasets heavily incorporate YouTube transcripts, descriptions, and metadata.
• Google's Gemini and AI Overviews directly reference YouTube content.
• Perplexity and ChatGPT both index and cite YouTube video content.
• YouTube transcripts are particularly valuable because they contain natural language mentions in conversational context, which aligns with how AI models process and generate text.

What to check:

• Brand YouTube channel: Does the brand have an active YouTube channel? How many subscribers? Video count? Upload frequency?
• Third-party video mentions: Are other YouTubers or channels mentioning the brand? In what context (reviews, tutorials, comparisons)?
• Video descriptions: Does the brand name appear in video descriptions of industry-relevant content?
• Video transcripts: Is the brand mentioned in spoken content of relevant videos? (AI models index transcripts)
• YouTube search presence: When searching "[brand name]" on YouTube, do results appear? Are they positive?
• Comment mentions: Is the brand mentioned in comments on relevant industry videos?

Scoring for YouTube (0-100):

2. Reddit Mentions -- High Correlation

Why Reddit matters:

• Reddit is one of the most heavily indexed platforms in AI training data (confirmed in Google's $60M/year Reddit licensing deal, 2024).
• AI systems heavily weight Reddit for product recommendations, comparisons, and user sentiment.
• "Reddit" is now appended to an estimated 10-15% of Google searches by users seeking authentic opinions.
• Perplexity frequently cites Reddit threads as sources.
• ChatGPT and Claude both reference Reddit discussions when answering product/service questions.

What to check:

• Subreddit presence: Is the brand discussed in relevant subreddits? Which ones?
• Mention volume: How many Reddit threads mention the brand? What is the trend (increasing/decreasing)?
• Sentiment: Are mentions mostly positive, negative, or neutral? What are common praise points and complaints?
• Official presence: Does the brand have an official Reddit account? Do they participate in discussions? Have they done AMAs?
• Recommendation threads: Does the brand appear in "What do you recommend for X?" threads? Is it the top recommendation or an also-ran?
• Subreddit community: Does the brand have its own subreddit? How active is it?

Scoring for Reddit (0-100):

3. Wikipedia Presence -- High Correlation

Why Wikipedia matters:

• Wikipedia is one of the highest-authority sources in AI training data. All major AI models have been trained on Wikipedia dumps.
• AI systems use Wikipedia as a primary source for entity recognition -- determining whether a brand is a "real" entity worth knowing about.
• Wikidata (Wikipedia's structured data sibling) provides machine-readable facts that AI models use for knowledge graph construction.
• Having a Wikipedia page is a strong signal of notability, which correlates with AI systems treating the brand as an authoritative entity.

What to check:

• Wikipedia page: Does the brand or company have its own Wikipedia article? Is it marked for deletion or quality issues?
• Founder page: Does the founder/CEO have a Wikipedia page? (Strong authority signal)
• Wikipedia citations: Is the brand's website cited as a reference in any Wikipedia articles?
• Wikidata entry: Does the brand have a Wikidata item (Q-number)? How complete is it?
• Wikipedia mentions: Is the brand mentioned in other Wikipedia articles (industry articles, competitor pages, category pages)?
• Article quality: If a Wikipedia page exists, is it a stub, start-class, or higher quality?

Scoring for Wikipedia (0-100):

4. LinkedIn Presence -- Moderate Correlation

Why LinkedIn matters:

• LinkedIn content is increasingly indexed by AI systems for professional and B2B context.
• Company LinkedIn pages and employee thought leadership posts build brand entity signals.
• AI models reference LinkedIn for company information, team credentials, and professional authority.
• LinkedIn articles and posts are indexed by search engines and AI crawlers.

What to check:

• Company page: Does the brand have a LinkedIn company page? Follower count? Post frequency?
• Employee thought leadership: Are employees (especially leadership) posting thought leadership content that mentions the brand?
• Company mentions: Is the brand mentioned in LinkedIn posts by non-employees? Industry analysts? Customers?
• LinkedIn articles: Are there long-form LinkedIn articles about or mentioning the brand?
• Employee profiles: Do employees list the company with detailed descriptions? Do they have strong professional profiles?
• Engagement metrics: What is the typical engagement (likes, comments, shares) on company posts?

Scoring for LinkedIn (0-100):

5. Other Platform Presence -- Supplementary

These platforms have lower but still meaningful correlation with AI visibility:

Quora

• Relevance: Quora answers are frequently included in AI training data and cited by Perplexity.
• What to check: Is the brand mentioned in Quora answers to industry-relevant questions? Does the brand have an official Quora presence?
• Signal strength: Moderate for B2C, lower for B2B.

Stack Overflow / Stack Exchange

• Relevance: Critical for developer-facing brands (SaaS, dev tools, APIs).
• What to check: Is the brand's product discussed in Stack Overflow questions/answers? Does the brand have a tag? Do they have an official account answering questions?
• Signal strength: High for technical products, irrelevant for most B2C.

GitHub

• Relevance: Critical for open-source and developer-focused brands.
• What to check: Does the brand have a GitHub organization? Stars on repositories? Mentions in other repos' documentation or discussions?
• Signal strength: High for dev tools and open-source, low for non-technical brands.

Industry Forums and Communities

• Relevance: Niche authority signals that AI models pick up from domain-specific training data.
• What to check: Is the brand discussed in industry-specific forums (e.g., Hacker News for tech, ProductHunt for startups, industry-specific Slack communities)?
• Signal strength: Moderate, but valuable for establishing niche authority.

News and Press

• Relevance: News mentions build entity authority and recency signals.
• What to check: Has the brand been covered by major news outlets or industry publications? How recently? What was the context?
• Signal strength: Moderate. Recency matters -- a mention in the last 6 months is far more valuable than one from 3 years ago.

Podcasts

• Relevance: Growing AI training data source. Transcripts are increasingly indexed.
• What to check: Has the brand or its leadership appeared on podcasts? Are podcast transcripts mentioning the brand indexed by search engines?
• Signal strength: Moderate and growing.

Composite Brand Authority Score

Scoring Formula

Formula:

Brand_Authority_Score = (YouTube * 0.25) + (Reddit * 0.25) + (Wikipedia * 0.20) + (LinkedIn * 0.15) + (Other * 0.15)

Score Interpretation

Analysis Procedure

Step 1: Identify Brand Information

Gather the following from the user or from the website:

• Brand name (exact spelling, including any official variants)
• Founder/CEO name(s)
• Domain URL
• Industry/category
• Key products or services (top 3)
• Key competitors (for comparison context)

Step 2: Platform Scanning

For each platform, use WebFetch to search and assess presence:

YouTube Check:

1. Search: [brand name] site:youtube.com
2. Check: youtube.com/@[brand-name] or youtube.com/c/[brand-name] for official channel
3. Search: "[brand name]" site:youtube.com (exact match for mentions in descriptions)
4. Note: Channel subscriber count, video count, latest upload date, third-party mention count

Reddit Check:

1. Search: [brand name] site:reddit.com
2. Search: "[brand name]" site:reddit.com (exact match)
3. Check: reddit.com/r/[brand-name] for official subreddit
4. Check: reddit.com/user/[brand-name] for official account
5. Note: Thread count, dominant subreddits, sentiment (positive/negative/neutral), recommendation frequency

Wikipedia Check (IMPORTANT — use BOTH methods to avoid false negatives):

Method 1 — Python API check (MOST RELIABLE, do this FIRST):

python3 -c "
import requests, json
from urllib.parse import quote_plus
brand = '[Brand_Name]'
# Check Wikipedia API directly
api_url = f'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={quote_plus(brand)}&format=json'
r = requests.get(api_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
data = r.json()
results = data.get('query', {}).get('search', [])
if results and brand.lower() in results[0].get('title', '').lower():
    print(f'WIKIPEDIA PAGE EXISTS: {results[0][\"title\"]}')
    print(f'URL: https://en.wikipedia.org/wiki/{results[0][\"title\"].replace(\" \", \"_\")}')