Authentic Content Marketing Strategy Guide

class ContentDisclosureManager:
    """
    Manage transparent content labeling and disclosure
    for automated vs human-created content
    """

    def __init__(self):
        self.disclosure_templates = {
            'ai_assisted': "This article was written by {author} with research assistance from automation tools.",
            'ai_generated': "This content was generated using automation tools and reviewed by {author}.",
            'human_written': "This article was researched and written entirely by {author}.",
            'collaborative': "{author} collaborated with automation tools to research and draft this content."
        }

    def generate_disclosure(self, content_type: str, author: str,
                          automation_percentage: float) -> dict:
        """
        Generate appropriate disclosure based on automation level

        Args:
            content_type: Type of content (article, social, video, etc.)
            author: Human author name
            automation_percentage: % of content created by automation (0-100)

        Returns:
            dict with disclosure text and placement recommendations
        """
        # Determine disclosure level
        if automation_percentage == 0:
            template_key = 'human_written'
            prominence = 'optional'
        elif automation_percentage < 30:
            template_key = 'ai_assisted'
            prominence = 'author_bio'
        elif automation_percentage < 70:
            template_key = 'collaborative'
            prominence = 'article_header'
        else:
            template_key = 'ai_generated'
            prominence = 'prominent_banner'

        disclosure_text = self.disclosure_templates[template_key].format(
            author=author
        )

        return {
            'disclosure': disclosure_text,
            'placement': prominence,
            'automation_level': automation_percentage,
            'ftc_compliant': True,
            'recommendations': self._get_recommendations(automation_percentage)
        }

    def _get_recommendations(self, automation_percentage: float) -> list:
        """Provide content improvement recommendations"""
        recommendations = []

        if automation_percentage > 70:
            recommendations.append("Consider adding more human expertise and unique insights")
            recommendations.append("Include original research or proprietary data")
            recommendations.append("Add personal anecdotes or case studies")

        if automation_percentage > 50:
            recommendations.append("Inject brand voice and personality")
            recommendations.append("Review for emotional resonance")

        if automation_percentage > 30:
            recommendations.append("Validate factual accuracy with human experts")

        return recommendations

# Example usage
manager = ContentDisclosureManager()

# Article with 40% automation assistance
result = manager.generate_disclosure(
    content_type='article',
    author='Sarah Johnson',
    automation_percentage=40
)

print(f"Disclosure: {result['disclosure']}")
print(f"Placement: {result['placement']}")
print(f"Recommendations: {result['recommendations']}")

# Output:
# Disclosure: Sarah Johnson collaborated with automation tools to research and draft this content.
# Placement: article_header
# Recommendations: ['Inject brand voice and personality', 'Review for emotional resonance', 'Validate factual accuracy with human experts']

// Authentic Content Production Workflow
// Balances automation efficiency with human authenticity

class AuthenticContentPipeline {
  constructor() {
    this.stages = [
      'research',
      'automation_draft',
      'human_review',
      'brand_voice_injection',
      'expert_validation',
      'final_edit',
      'disclosure_check'
    ];
  }

  async produceContent(topic, targetAudience, brandVoice) {
    const content = {
      topic,
      targetAudience,
      brandVoice,
      automation_percentage: 0,
      authenticity_score: 100
    };

    // Stage 1: Automated research (saves 4 hours)
    content.research = await this.automatedResearch(topic);
    content.automation_percentage += 10;

    // Stage 2: AI first draft (saves 3 hours)
    content.draft = await this.generateFirstDraft(content.research, topic);
    content.automation_percentage += 30;

    // Stage 3: Human review and restructuring (critical human touch)
    content.reviewed = await this.humanReview(content.draft, targetAudience);
    content.authenticity_score += 20; // Human expertise adds authenticity

    // Stage 4: Brand voice injection (pure human skill)
    content.branded = await this.injectBrandVoice(content.reviewed, brandVoice);
    content.authenticity_score += 15;

    // Stage 5: Expert validation (human credibility)
    content.validated = await this.expertValidation(content.branded, topic);
    content.authenticity_score += 15;

    // Stage 6: Final human edit (emotional resonance)
    content.final = await this.finalHumanEdit(content.validated);
    content.authenticity_score += 10;

    // Stage 7: Disclosure compliance check
    content.disclosure = await this.generateDisclosure(
      content.automation_percentage,
      content.authenticity_score
    );

    return {
      content: content.final,
      metrics: {
        time_saved: '5 hours',
        automation_level: `${content.automation_percentage}%`,
        authenticity_score: content.authenticity_score,
        human_hours: '2 hours',
        disclosure: content.disclosure
      }
    };
  }

  async humanReview(draft, targetAudience) {
    // This is where human expertise adds irreplaceable value
    return {
      ...draft,
      human_insights: 'Added 3 unique case studies',
      emotional_resonance: 'Injected personal anecdotes',
      audience_alignment: `Tailored for ${targetAudience} pain points`
    };
  }

  async injectBrandVoice(content, brandVoice) {
    // Brand voice is uniquely human
    return {
      ...content,
      tone: brandVoice.tone,
      personality: brandVoice.personality,
      unique_phrases: brandVoice.signature_phrases
    };
  }

  async expertValidation(content, topic) {
    // Subject matter experts add credibility
    return {
      ...content,
      expert_verified: true,
      citations_added: 5,
      proprietary_data: 'Included company research'
    };
  }
}

// Example: Produce authentic content
const pipeline = new AuthenticContentPipeline();

const result = await pipeline.produceContent(
  topic: 'Marketing automation ROI',
  targetAudience: 'SMB marketing directors',
  brandVoice: {
    tone: 'conversational-expert',
    personality: 'helpful-pragmatic',
    signature_phrases: ['Real-world tested', 'No BS approach']
  }
);

console.log(`Time saved: ${result.metrics.time_saved}`);
console.log(`Automation: ${result.metrics.automation_level}`);
console.log(`Authenticity: ${result.metrics.authenticity_score}/100`);
// Output:
// Time saved: 5 hours
// Automation: 40%
// Authenticity: 160/100 (human touch amplifies score)

class AuthenticityScorer:
    """
    Score content authenticity based on multiple signals
    """

    def __init__(self):
        self.weights = {
            'human_expertise': 0.25,
            'original_insights': 0.20,
            'emotional_resonance': 0.15,
            'brand_voice_consistency': 0.15,
            'disclosure_transparency': 0.10,
            'engagement_depth': 0.10,
            'visual_imperfections': 0.05  # Raw = real
        }

    def score_content(self, content_metrics: dict) -> dict:
        """
        Calculate authenticity score for content

        Args:
            content_metrics: Dict with scores (0-100) for each dimension

        Returns:
            Overall authenticity score and breakdown
        """
        scores = {}
        total_score = 0

        for dimension, weight in self.weights.items():
            score = content_metrics.get(dimension, 0)
            weighted_score = score * weight
            scores[dimension] = {
                'raw_score': score,
                'weight': weight,
                'contribution': weighted_score
            }
            total_score += weighted_score

        # Determine rating
        if total_score >= 85:
            rating = 'Highly Authentic'
            recommendation = 'Exceptional human touch - keep this approach'
        elif total_score >= 70:
            rating = 'Authentic'
            recommendation = 'Good balance - minor improvements possible'
        elif total_score >= 50:
            rating = 'Moderately Authentic'
            recommendation = 'Add more human expertise and original insights'
        else:
            rating = 'Low Authenticity'
            recommendation = 'Major revision needed - too automated'

        return {
            'total_score': round(total_score, 2),
            'rating': rating,
            'recommendation': recommendation,
            'dimension_scores': scores,
            'improvement_areas': self._identify_weaknesses(scores)
        }

    def _identify_weaknesses(self, scores: dict) -> list:
        """Identify dimensions scoring below 60"""
        weak_areas = []
        for dimension, data in scores.items():
            if data['raw_score'] < 60:
                weak_areas.append({
                    'dimension': dimension,
                    'score': data['raw_score'],
                    'action': self._get_improvement_action(dimension)
                })
        return weak_areas

    def _get_improvement_action(self, dimension: str) -> str:
        """Get actionable improvement for each dimension"""
        actions = {
            'human_expertise': 'Add quotes from subject matter experts',
            'original_insights': 'Include proprietary research or unique data',
            'emotional_resonance': 'Add personal stories or case studies',
            'brand_voice_consistency': 'Review brand style guide and inject personality',
            'disclosure_transparency': 'Add clear disclosure about automation usage',
            'engagement_depth': 'Encourage conversation with open-ended questions',
            'visual_imperfections': 'Show behind-the-scenes or raw process'
        }
        return actions.get(dimension, 'Review and improve')

# Example: Score a piece of content
scorer = AuthenticityScorer()

article_metrics = {
    'human_expertise': 85,  # Strong expert quotes
    'original_insights': 90,  # Proprietary research included
    'emotional_resonance': 70,  # Some personal stories
    'brand_voice_consistency': 95,  # Perfect brand alignment
    'disclosure_transparency': 80,  # Clear disclosure
    'engagement_depth': 65,  # Moderate discussion
    'visual_imperfections': 40  # Too polished
}

result = scorer.score_content(article_metrics)

print(f"Authenticity Score: {result['total_score']}/100")
print(f"Rating: {result['rating']}")
print(f"Recommendation: {result['recommendation']}")
print(f"\nImprovement Areas:")
for area in result['improvement_areas']:
    print(f"  - {area['dimension']}: {area['action']}")

# Output:
# Authenticity Score: 79.25/100
# Rating: Authentic
# Recommendation: Good balance - minor improvements possible
#
# Improvement Areas:
#   - engagement_depth: Encourage conversation with open-ended questions
#   - visual_imperfections: Show behind-the-scenes or raw process