https://github.com/ritvik19/clickbait-detection
https://github.com/ritvik19/clickbait-detection
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/ritvik19/clickbait-detection
- Owner: Ritvik19
- Created: 2020-06-26T04:53:27.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-09T01:22:19.000Z (almost 6 years ago)
- Last Synced: 2025-01-23T06:14:54.935Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 2.85 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Clickbait-Detection
Machine Learning based Clickbait Detector
Clickbait is basically some content on the Internet whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page.
Although clickbaits can be in the form of texts or thumbnails these model detect text based Clickbaits
## Data Sources
* [Kaggle: clickbait-dataset](https://www.kaggle.com/amananandrai/clickbait-dataset)
* [Kaggle: news-clickbait](https://www.kaggle.com/micdsouz/news-clickbait)
* [Webis-clickbait-16](https://webis.de/data/webis-clickbait-16.html)
* [RVCJ](https://www.rvcj.com/) `crawled`
* [Viral Nova](https://viralnova.com/) `crawled`
* [Viral Stories](http://viralstories.in/) `crawled`
* [Inshorts](https://inshorts.com/en/read) `crawled`
Crawler : [Lord Varys](https://github.com/Ritvik19/Lord-Varys)
## Model Performances
Model | Accuracy | AUROC
---|---:|---:
Logisitic Regression | 0.8745 | 0.9431
Multinomimal Naive Bayes + Oversampling | 0.8561 | 0.9342
Random Forest | 0.8448 | 0.9060
Bagging Logistic Regression | 0.8760 | 0.9443