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.idea/
Jup Files/
static/
templates/
News_Detection.ipynb
News_Detection.py
app.ipynb
app.py
finalized_model.sav
model.pickle
news.csv

# Paper Publication https://ieeexplore.ieee.org/document/9743010 # Fake-News-Detector A basic machine learning based News detector to identify the authencity of the news articles. Algorithms like Pasiive-Aggressive, Naive Bayes, Support Vector Machine and Random Forest were used # Overview The topic of fake news detection on social media has recently attracted tremendous attention. The basic countermeasure of comparing websites against a list of labeled fake news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Natural Language Processing to detect fake news directly, based on the text content of news articles. # Dataset train.csv: A full training dataset with the following attributes: id: unique id for a news article title: the title of a news article author: author of the news article text: the text of the article; could be incomplete label: a label that marks the article as potentially unreliable 1: unreliable 0: reliable test.csv: A testing training dataset with all the same attributes at train.csv without the label. # Steps Clone the repo to your local machine- > git clone git://github.com/Prakassh1/Fake-News-Detector.git > cd Fake-News-Detector Make sure you have all the dependencies installed- python 3.6+ numpy tensorflow gensim pandas nltk For nltk, we recommend typing python.exe in your command line which will take you to the Python interpretor. Then, enter- >>> import nltk >>> nltk.download()} You're good to go now- > python svm.py # Dataset Repo 1.Datasets from Kaggle

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