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Fake News Detection in Python
Fake News Detection in Python
In this project, we have used various natural language processing techniques and machine learning algorithms to classify fake news articles using sci-kit libraries from python.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
What things you need to install the software and how to install them:
pip install -U scikit-learn
pip install numpy
pip install scipy
conda install -c scikit-learn
conda install -c anaconda numpy
conda install -c anaconda scipy
The data source used for this project is LIAR dataset which contains 3 files with .tsv format for test, train and validation. Below is some description about the data files used for this project.
LIAR: A BENCHMARK DATASET FOR FAKE NEWS DETECTION
William Yang Wang, "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection, to appear in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), short paper, Vancouver, BC, Canada, July 30-August 4, ACL.
the original dataset contained 13 variables/columns for train, test and validation sets as follows:
To make things simple we have chosen only 2 variables from this original dataset for this classification. The other variables can be added later to add some more complexity and enhance the features.
Below are the columns used to create 3 datasets that have been in used in this project
You will see that newly created dataset has only 2 classes as compared to 6 from original classes. Below is method used for reducing the number of classes.
The dataset used for this project were in csv format named train.csv, test.csv and valid.csv and can be found in repo. The original datasets are in "liar" folder in tsv format.
This file contains all the pre processing functions needed to process all input documents and texts. First we read the train, test and validation data files then performed some pre processing like tokenizing, stemming etc. There are some exploratory data analysis is performed like response variable distribution and data quality checks like null or missing values etc.
In this file we have performed feature extraction and selection methods from sci-kit learn python libraries. For feature selection, we have used methods like simple bag-of-words and n-grams and then term frequency like tf-tdf weighting. we have also used word2vec and POS tagging to extract the features, though POS tagging and word2vec has not been used at this point in the project.
Here we have build all the classifiers for predicting the fake news detection. The extracted features are fed into different classifiers. We have used Naive-bayes, Logistic Regression, Linear SVM, Stochastic gradient descent and Random forest classifiers from sklearn. Each of the extracted features were used in all of the classifiers. Once fitting the model, we compared the f1 score and checked the confusion matrix. After fitting all the classifiers, 2 best performing models were selected as candidate models for fake news classification. We have performed parameter tuning by implementing GridSearchCV methods on these candidate models and chosen best performing parameters for these classifier. Finally selected model was used for fake news detection with the probability of truth. In Addition to this, We have also extracted the top 50 features from our term-frequency tfidf vectorizer to see what words are most and important in each of the classes. We have also used Precision-Recall and learning curves to see how training and test set performs when we increase the amount of data in our classifiers.
Our finally selected and best performing classifier was Logistic Regression which was then saved on disk with name final_model.sav. Once you close this repository, this model will be copied to user's machine and will be used by prediction.py file to classify the fake news. It takes an news article as input from user then model is used for final classification output that is shown to user along with probability of truth.
Below is the Process Flow of the project:
Below is the learning curves for our candidate models.
Logistic Regression Classifier
Random Forest Classifier
As we can see that our best performing models had an f1 score in the range of 70's. This is due to less number of data that we have used for training purposes and simplicity of our models. For the future implementations, we could introduce some more feature selection methods such as POS tagging, word2vec and topic modeling. In addition, we could also increase the training data size. We will extend this project to implement these techniques in future to increase the accuracy and performance of our models.
A step by step series of examples that tell you have to get a development env running
$ git clone https://github.com/nishitpatel01/Fake_News_Detection.git
This will copy all the data source file, program files and model into your machine.
Prerequisites sections then open the anaconda prompt, change the directory to the folder where this project is saved in your machine and type below command and press enter.cd C:/your cloned project folder path goes here/
prediction.py file, To do this, run below command in anaconda prompt.python prediction.py
After hitting the enter, program will ask for an input which will be a piece of information or a news headline that you want to verify. Once you paste or type news headline, then press enter.
Once you hit the enter, program will take user input (news headline) and will be used by model to classify in one of categories of "True" and "False". Along with classifying the news headline, model will also provide a probability of truth associated with it.
If you have chosen to install python (and did not set up PATH variable for it) then follow below instructions:
cd C:/your cloned project folder path goes here/
python.exe in your machine. you can search this in window explorer search bar.python.exe path, you need to write whole path of it and then entire path of project folder with prediction.py at the end. For example if your python.exe is located at c:/Python36/python.exe and project folder is at c:/users/user_name/desktop/fake_news_detection/, then your command to run program will be as below:c:/Python36/python.exe C:/users/user_name/desktop/fake_news_detection/prediction.py
After hitting the enter, program will ask for an input which will be a piece of information or a news headline that you want to verify. Once you paste or type news headline, then press enter.
Once you hit the enter, program will take user input (news headline) and will be used by model to classify in one of categories of "True" and "False". Along with classifying the news headline, model will also provide a probability of truth associated with it. It might take few seconds for model to classify the given statement so wait for it.
Selected from shared topics, language and repository description—not editorial ratings.
alinasir1596 /
This Repository contains a Jupyter Notebook and 2 Datasets of Training and Testing used for Fake News Classification and Detection using Machine Learning Models.
OtoYuki /
A machine learning project for fake news detection using the LIAR dataset. This repository includes Jupyter notebooks for data preprocessing, exploratory data analysis, training, and evaluation of classification models like Logistic Regression, SVM, and Random Forest.
Dycouzt /
If you have chosen to install python (and already setup PATH variable for python.exe) then follow instructions:
cd C:/your cloned project folder path goes here/
python.exe C:/your cloned project folder path goes here/
After hitting the enter, program will ask for an input which will be a piece of information or a news headline that you want to verify. Once you paste or type news headline, then press enter.
Once you hit the enter, program will take user input (news headline) and will be used by model to classify in one of categories of "True" and "False". Along with classifying the news headline, model will also provide a probability of truth associated with it.
A Jupyter Notebook that detects fake news using a fine-tuned RoBERTa transformer model. Trains on real-world datasets with tokenization, vectorization, and text classification. Explores NLP pipeline performance, model accuracy, and real-time inference.
Dineshh9515 /
This project is a Python-based Jupyter Notebook that explores NLP and deep learning techniques. It demonstrates how traditional text preprocessing and modern transformer-based models can be combined for advanced language understanding tasks such as tokenization, part-of-speech tagging, semantic analysis, and text classification.