VivaKitClickbait & Misleading Headline Detection System
Production machine learning headline integrity analyzer and viral clickbait classifier. Built with Scikit-learn under a 100% commercially compliant BSD-3-Clause license (zero copyleft restrictions). Evaluated on 6,400 held-out news headlines from a 32,000-sample balanced benchmark dataset (CC BY 4.0). Delivers 97.45% accuracy and 0.9960 ROC AUC with sub-millisecond CPU execution (>10,000 headlines/sec), featuring explainable linguistic cue extraction and interactive risk tiering.
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Our honest results
| Classification Accuracy | 97.45% (Held-out 6,400 test headlines) |
| ROC AUC Score | 0.9960 (Outstanding discriminatory power) |
| Clickbait Precision | 97.80% (Minimal false alarms on legitimate news) |
| Clickbait Recall | 97.09% (Catches 97%+ of viral clickbait headlines) |
| CPU Inference Latency | < 0.05 ms/headline (>10,000 headlines/sec on standard CPU) |
Evaluated strictly on 6,400 held-out real news headlines (CC BY 4.0 / CC0). Includes 0.45 MB lightweight trained model weights, 32,000-headline benchmark dataset, Gradio 6 console with interactive headline inspector and trigger n-gram tables, automated pytest suite, and 15 viva voce questions on TF-IDF, logistic loss, curiosity gaps, and journalistic ethics.
What's included
- Readable source codeSmall, commented modules you can explain line by line in your viva.
- Demo appA local web app to show your examiners - runs offline on your laptop.
- Trained model + reference resultsSo you can compare your own runs fairly.
- Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
- One-click setup & testsWindows setup script and tests that prove it works on your PC.
- Dataset download scriptFetches the public data from its original source, with the citation.
Dataset credit
Clickbait Title Classification Benchmark Dataset from Chakrabarti et al. (CC BY 4.0 / CC0 Public Domain).
Academic integrity
We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.