| --- |
| title: CEAR – Cultural Exposure & Algorithmic Risk Analyzer |
| emoji: 📡 |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 6.1.0 |
| app_file: app.py |
| license: mit |
| --- |
| |
| # CEAR – Cultural Exposure & Algorithmic Risk Analyzer |
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| CEAR is a transparent, rule-based model and Hugging Face Space that helps users understand their social media habits through three interpretable metrics: |
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| * **Cultural Connectedness (C-Score)** – approximate trend exposure |
| * **Algorithmic Risk (A-Risk)** – attention concentration in algorithm-driven feeds |
| * **Diversity Index (D-Index)** – distribution of time across platforms |
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| The project combines an analytic scoring engine with a clean Gradio interface. It does not use machine learning but functions as an interpretable behavioral analysis model. |
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| --- |
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| ## 🚀 Live Demo |
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| Use CEAR directly in your browser: |
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| **Hugging Face Space:** `https://huggingface.co/spaces/<your-username>/CEAR` |
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| --- |
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| ## 📦 Project Structure |
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| ``` |
| ├── app.py # Gradio interface and interpretation logic |
| ├── cear_model.py # Core scoring engine (C/A/D + efficiency) |
| ├── platform_weights.json # Hand-tuned theoretical platform weights |
| ├── requirements.txt # Dependencies |
| └── README.md # This file |
| ``` |
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| --- |
|
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| ## 🎯 Purpose |
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| CEAR serves two goals: |
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| 1. Provide an **interpretable framework** for analyzing social media behavior. |
| 2. Demonstrate a **fully-documented model card**, Gradio deployment, and rule-based transform suitable for academic or instructional settings. |
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| It is ideal for: |
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| * Students learning model documentation |
| * Researchers exploring rule-based analytics |
| * Users who want insight into their social media patterns |
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| --- |
|
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| ## 📥 Inputs |
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| CEAR accepts values for **7 platform buckets**: |
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| * TikTok |
| * Instagram |
| * YouTube |
| * Twitter/X |
| * Reddit |
| * Facebook |
| * Other |
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| For each platform, the user inputs: |
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| * **Minutes per week** |
| * **Variety score** (0–10) |
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| Global self-reports: |
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| * **Feed satisfaction** (0–10) |
| * **FOMO / Out-of-the-loop feeling** (0–10) |
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| ### Input Validation Rules |
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| * Platforms with **0 minutes** are excluded from calculations. |
| * Variety > 0 while minutes = 0 triggers a **warning** and is ignored. |
| * Negative values are treated as zero. |
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| --- |
|
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| ## 🧮 Model Logic |
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| CEAR is a rule-based model driven by theoretical platform weights. |
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| ### Platform Weights |
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| Each platform has: |
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| * `W_C` – Cultural Connectedness Weight |
| * `W_A` – Algorithmic Risk Weight |
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| Defined in `platform_weights.json`. |
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| ### Score Calculations |
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| #### 1. **C-Score (Cultural Connectedness)** |
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| Uses a log transform to encode diminishing returns: |
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| ``` |
| C_contrib = W_C * log10(minutes + 1) |
| ``` |
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| #### 2. **A-Risk (Algorithmic Risk)** |
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| Linear with respect to time: |
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| ``` |
| A_contrib = W_A * minutes |
| ``` |
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| #### 3. **D-Index (Platform Diversity)** |
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| Based on the inverse Herfindahl index: |
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| ``` |
| s_i = minutes_i / total_minutes |
| D_Index = 1 / sum(s_i^2) |
| ``` |
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| #### 4. **Per-Platform Cultural Efficiency (0–100)** |
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| ``` |
| eff_raw = C_contrib / minutes |
| normalized = eff_raw / max(eff_raw) * 100 |
| ``` |
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| #### 5. **Average Variety (Weighted)** |
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| ``` |
| Avg_Variety = mean(variety_score, weighted by minutes) |
| ``` |
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| ### Interpretation Logic |
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| * Satisfaction and FOMO do **not** influence the numeric scores. |
| * Instead, they shape the **narrative summary**. |
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| --- |
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| ## 🧩 Output Sections |
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| The app produces three final outputs: |
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| ### 1. **CEAR Analysis Summary** |
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| Includes: |
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| * C-Score |
| * A-Risk |
| * D-Index |
| * Average Variety |
| * Self-report reflections |
| * Warnings for invalid input patterns |
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| ### 2. **Interpretation Narrative** |
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| A human-readable explanation linking: |
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| * Platform mix |
| * Variety |
| * Satisfaction |
| * FOMO |
| * Risk and connectedness profiles |
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| ### 3. **Platform Efficiency Breakdown** |
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| Both as: |
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| * A ranked markdown list (easy to read) |
| * A numeric DataFrame (for analysis) |
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| --- |
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| ## 🧪 Validation |
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| Since CEAR is deterministic, validation focuses on correctness: |
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| * Unit tests confirm expected behavior for high-concentration vs balanced usage. |
| * Manual tests confirm variety weighting, reset logic, and warnings. |
| * The scoring formulas are transparent and reproducible. |
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| --- |
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| ## ⚠️ Limitations |
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| CEAR is **not** a predictive model. |
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| * It does not infer real cultural exposure. |
| * It cannot evaluate actual content. |
| * Weights reflect reasonable theoretical assumptions, not empirical fitting. |
| * It does not diagnose mental health or prescribe usage patterns. |
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| The model is best used for **reflection**, **education**, and **exploration**. |
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| --- |
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| ## 🔧 Running Locally |
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| You can run the app locally: |
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| ```bash |
| pip install -r requirements.txt |
| python app.py |
| ``` |
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| Or import the model: |
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| ```python |
| from cear_model import CEARModel |
| import pandas as pd |
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| model = CEARModel() |
| df = pd.DataFrame([ |
| {"platform_name": "tiktok", "minutes_per_week": 300, "variety_score": 4}, |
| ]) |
| print(model.calculate_scores(df, satisfaction=6, fomo=4)) |
| ``` |
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| --- |
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| ## 📜 License |
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| MIT License |
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| --- |
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| ## 🙌 Acknowledgments |
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| This project was built to demonstrate: |
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| * Transparent model design |
| * Clear model documentation |
| * Proper Hugging Face Space structure |
| * User-oriented interpretability |
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| Feel free to fork and extend with: |
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| * Empirical weights |
| * Trend detection |
| * Behavioral clustering |
| * Recommendation strategies |
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| CEAR v1.0 is a foundation for deeper exploration into how we relate to algorithmic feeds. |
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