Social Intelligence

Social Intelligence

When to Use

Trigger phrases:

• "What are people saying about X on social media?"
• "Research sentiment around [product/topic]"
• "Find viral content patterns in [niche]"
• "What's trending on Twitter/Reddit/YouTube?"
• "Competitive intelligence on [competitor]"
• "User pain points in [category]"
• "Social proof for [feature/product]"

Use cases:

• Pre-launch market research
• Competitor positioning analysis
• Content ideation from social trends
• Brand sentiment monitoring
• Product feedback aggregation
• Influencer discovery
• Viral hook mining

When NOT to use: Simple single-platform queries (use native tools), when ethical scraping boundaries crossed, when historical data >6 months old (use archived datasets instead)

When NOT to Use

• When the answer is already known and documented
• For time-sensitive decisions that cannot wait for thorough research
• When the topic is outside your domain of competence

Overview

Social Intelligence enables thorough investigation with structured methodology.

Workflow

# Example: Source evaluation
def evaluate_source(url: str) -> dict:
    return {
        "authority": check_domain_authority(url),
        "currency": get_last_updated(url),
        "objectivity": detect_bias(url),
        "accuracy": cross_reference(url),
    }

1. Define question — Clarify the research objective
2. Gather sources — Collect primary and secondary data
3. Analyze — Apply analytical frameworks to findings
4. Synthesize — Combine insights into actionable conclusions
5. Present — Deliver findings in clear, compelling format
6. Archive — Store research for future reference

Source Evaluation

• Authority — Is the source credible and expert?
• Currency — Is the information recent and relevant?
• Objectivity — Is there bias or conflict of interest?
• Accuracy — Can claims be verified independently?

Output Format

• Executive summary (1-2 paragraphs)
• Key findings (bullet points)
• Detailed analysis (sections with evidence)
• Recommendations (actionable next steps)
• Sources and methodology

Anti-Rationalization Table

Process

1. Research — Analyze target audience, competitors, and trending topics
2. Create — Generate content following brand guidelines and best practices
3. Publish & Optimize — Distribute to target platforms, track performance, iterate

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings