Upgrading TKLLM with TikTok’s Content Viral Propagation Model

1. Objective Overview (What & Why)

The goal is to enhance TKLLM with TikTok-style content virality intelligence. Instead of only generating stylistically consistent scripts, TKLLM becomes an algorithm-aware content generator capable of producing content with high virality potential.

Detailed objectives:

1. Data Filtering Layer (High-Value Sample Selection)

2. Use ViralScore to identify historical posts with the highest propagation potential.

3. Assign higher training weight to these posts.

4. Reduces noise in the training set and emphasizes patterns correlated with virality.

5. Training Objective Layer (Virality as Signal)

6. Integrate virality signals as an auxiliary loss or reward function.

7. Encourages the model to internalize features that drive shares, comments, replays, and watch-through rates.

8. Supports both multi-task learning and optional reinforcement learning approaches.

9. Generation-Time Optimization (Decoding & Reranking)

10. Apply ViralScore at decoding: rerank candidate outputs, bias sampling probability, or constrain decoding toward high-virality styles.

11. Achieves virality improvements without full retraining.

12. Evaluation & Feedback Loop

13. Build offline metrics (predicted vs actual virality) and online A/B tests.

14. Close the loop by feeding real engagement back to the model for continual improvement.

Key Principle: ViralScore augments TKLLM without compromising language quality or controllability.

2. System Architecture (High-Level)

[Raw TikTok Data / Creators] -> Data Lake -> Feature Extraction -> ViralScore Estimator
                              ↘                         ↘
                               ↘                    High-Value Sample Selector
                                ↘                         ↘
                             Style Embedding DB --------> TKLLM Training Pipeline (GSPF)
                                                       ↘
                                                        ↘
                                                     Generation API -> Reranker (Viral Reward)
                                                       ↘
                                                      Publish / A-B Test -> Online Feedback -> Metrics DB

Component Details:

1. Raw TikTok Data / Creators

2. Historical videos, titles, captions, hashtags, engagement metrics, audio/visual content.

3. Data Lake

4. Centralized repository storing raw multimodal data for large-scale batch processing.

5. Feature Extraction

6. Text: BERT/GPT embeddings, token counts, sentiment scores.

7. Visual: CLIP/ViT embeddings of key frames.
8. Audio: MFCC features, rhythm, emotional spectrum.

9. Temporal/behavioral: CTR, watch-through, replays, early view trajectories.

10. ViralScore Estimator

11. Predicts potential virality for a given post.

12. Multimodal Transformer-based encoder, outputs scalar ViralScore and subcomponent breakdowns (E, H, R, S, T).

13. High-Value Sample Selector

14. Applies thresholds or weighted sampling based on ViralScore.

15. Outputs priority training set.

16. Style Embedding DB

17. Stores latent style embeddings for creator-specific replication.

18. TKLLM Training Pipeline (GSPF)

19. Gradient Surgical Fine-tuning with sample weighting or auxiliary viral loss.

20. Supports incremental updates using online feedback.

21. Generation API

22. Produces N candidate scripts for a given prompt.

23. Viral Reward Reranker

24. Re-ranks candidates according to ViralScore.

25. Optionally applies reward-augmented decoding.

26. Online Feedback & Metrics DB

    • Collects real-world engagement data to continuously refine ViralScore and LM.

3. Viral Model Design (Core Algorithm)

3.1 ViralScore Components

Score Calculation:

\[ V = \sigma(\alpha E + \beta H + \gamma R + \delta S + \epsilon T) \]

• \(\sigma\) = squashing function (sigmoid, scaled softmax)
• Coefficients \(\alpha\ ... \epsilon\) = learned via regression or tuned heuristically

3.2 Model Inputs (Multimodal)

• Textual Features

• Titles, captions, comments embeddings

• Topic vectors, sentiment, word counts

• Visual Features

• First/ key frame embeddings via CLIP/ViT

• Motion / scene complexity metrics

• Audio Features

• Rhythm, pitch variance, energy levels

• Music genre embeddings

• Behavioral Signals

• Early CTR, watch-through rate, shares, replays

• Temporal/Trend Features

• Posting hour, weekday, hashtag trend strength, geographic info

3.3 Architecture Recommendations

1. Multimodal Encoder

2. Independent encoders per modality

3. Cross-attention fusion layer to capture multimodal interactions

4. Temporal Module (Optional)

5. 1D CNN or Temporal Transformer to capture emotion or engagement dynamics over time

6. MLP Head

7. Predicts ViralScore scalar and optionally subcomponent scores (E, H, R, S, T)

8. Training Objectives

9. Supervised regression on real propagation metrics

10. Optional rank-based loss for pairwise ranking of content
11. Multi-task auxiliary loss for subcomponents

4. Training Data & Labels

4.1 Label Sources

1. Real Behavior Signals (High Quality)

2. 24h / 72h post-release plays, shares, comments

3. Watch-through rate and rewatch frequency

4. Platform Proxy Signals (Weak Supervision)

5. Early small-batch CTR, early replays, early engagement metrics

6. Human Labels (Optional for Cold Start)

7. Experts annotate hook strength, replicability, trend alignment

4.2 Sample Construction

• Positive samples: top X% viral content
• Negative samples: low engagement or failed content
• Balanced stratification by content type: drama, education, e-commerce, dance

4.3 Data Augmentation

• Text perturbation: replace CTAs, rephrase hooks
• Temporal sampling: early engagement sequences, multiple time windows
• Synthetic “mirrored” challenges: minor variant content to improve replicability detection

5. Integrating ViralScore into TKLLM Training

Entry Point A — Sample Weighting

\[ \mathcal{L} = \frac{1}{N} \sum_i w_i \cdot \mathcal{L}_{CE}(y_i, \hat{y}_i), \quad w_i = 1 + \lambda V_i \]

• Biases LM towards high-virality patterns
• Easy to implement with GSPF

Entry Point B — Auxiliary Loss

\[ \mathcal{L} = \mathcal{L}*{LM} + \mu \cdot \mathcal{L}*{Viral}, \quad \mathcal{L}_{Viral} = \text{MSE}(V, \hat{V}) \]

• Internal representation learns virality-aware semantics
• Multi-task learning improves transfer to unseen content

Entry Point C — Generation-Time Reward / Reranking

1. Reranking: generate N candidates → compute ViralScore → select top
2. Reward-Augmented Decoding:

\[ \text{score}(c) = \log P_{LM}(c) + \eta \cdot \text{ViralScore}(c) \]

1. RL Fine-Tuning (optional, cautious): PPO/TRPO using ViralScore as reward
2. Benefits: improves output without full retraining, flexible deployment

6. Training Implementation (Pseudocode)

6.1 ViralScore Training

model = MultiModalViralModel()
optimizer = Adam(model.parameters(), lr=1e-4)

for epoch in range(EPOCHS):
    for batch in loader:
        features, labels = batch
        preds = model(features)
        loss = mse(preds, labels)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

6.2 GSPF Fine-Tuning with Weighted Samples

for epoch in range(EPOCHS):
    for batch in data_loader:
        inputs, targets, viral_scores = batch
        outputs = model(inputs)
        lm_loss = cross_entropy(outputs, targets, reduction='none')  # per-token
        sample_loss = lm_loss.mean(dim=1)
        weights = 1.0 + lambda_v * viral_scores
        weighted_loss = (weights * sample_loss).mean()
        weighted_loss.backward()
        optimizer.step()
        optimizer.zero_grad()

6.3 Generation & Rerank

def generate_with_rerank(prompt, model, viral_estimator, k=16):
    cands = model.sample(prompt, num_samples=k, temperature=0.9)
    scored = []
    for cand in cands:
        features = extract_features(cand)
        vscore = viral_estimator.predict(features)
        score = logprob(model, cand) + eta * vscore
        scored.append((score, cand, vscore))
    best = max(scored, key=lambda x: x[0])
    return best[1], best[2]

7. Evaluation & A/B Testing

Offline Metrics

• ViralScore MSE / Spearman: prediction accuracy of propagation
• Perplexity / BLEU / ROUGE: language quality
• LM+Viral combined ranking: human evaluation for viral appeal

Online Metrics (A/B Test)

• A (baseline): vanilla TKLLM outputs
• B (Viral-augmented): sample-weighted + rerank

KPIs:

• Day-1 play growth (Δ plays)
• Watch-through rate
• Share/forward rate
• Rewatch rate
• Conversion rate (click/purchase)

Statistical significance: bootstrap / t-test.

8. Deployment & Inference Optimization

• Deploy Viral Estimator as REST/GRPC microservice
• Model compression: quantization (8/4-bit) or distillation
• Batch evaluation: generate N candidates → batch scoring → rerank
• Latency control: two-stage lightweight+heavy scoring; rerank <500ms for 15s scripts

9. Online Feedback & Learning Loop

• Data Collection: 1h/24h engagement metrics
• Sample Pool Refresh: automated high-virality sample inclusion
• Incremental Training: GSPF dynamic freezing for low-cost updates
• Iteration Cycle: stabilize metrics → expand A/B → full rollout

10. Safety, Ethics & Abuse Prevention

• Abuse Prevention: avoid incentivizing controversy or misinformation
• BrandSafety / PolicyPenalty: sensitive content reduces ViralScore or is blocked
• Controlled RL: constrained decoding, topic/word blacklists
• Transparency: subscore breakdown (E,H,R,S,T) for auditing