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75 lines
1.9 KiB
75 lines
1.9 KiB
[](https://codefirst.iut.uca.fr/PyPloteam/PlotaFakeNews)
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### Built with
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# Fake News Detector
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## Getting started
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Clone repo:
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```shell
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git clone https://codefirst.iut.uca.fr/git/PyPloteam/PlotaFakeNews.git
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```
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Install requirements using:
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```shell
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pip install -r requirements.txt
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```
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## Training script
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```shell
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cd src/
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```
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```shell
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python3 main.py
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```
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## Launch web app
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```shell
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cd src/app/
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```
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```shell
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python3 manage.py runserver
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```
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## Dataset informations
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[FakeNews Dataset](https://www.kaggle.com/datasets/algord/fake-news)
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| **title** | **news_url** | **source_domain** | _tweet_num_ | **real** |
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|---|---|---|---|---|
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| title of the article | URL of the article | web domain where article was posted | _number of retweets for this article_ | 1 is real and 0 is fake |
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_(N.B. Italic columns are not used for learning models)_
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## References
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- [scikit-learn models](https://scikit-learn.org/stable/supervised_learning.html)
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- [Label encoder _with scikit-learn_](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)
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- [Django documentation](https://www.djangoproject.com/en/5.0/)
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- [Datas provenance](https://github.com/KaiDMML/FakeNewsNet)
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## Authors
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[LIVET Hugo](https://codefirst.iut.uca.fr/git/hugo.livet)
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[DE LA FUENTE Axel](https://codefirst.iut.uca.fr/git/axel.de_la_fuente)
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## Acknowledgements
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Thanks to our professor for his guidance and feedback throughout the development of this project.
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- SAMIR Chafik |