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https://github.com/fyt3rp4til/fake-news-detection-v3-word2vec-spacy


https://github.com/fyt3rp4til/fake-news-detection-v3-word2vec-spacy

glove-embeddings knn-classifier multinomial-naive-bayes spacy word2vec

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README 

        
# 📰 Fake News Classification Project



## 📌 Table of Contents

1. [Problem Statement](#-problem-statement)

2. [Dataset](#-dataset)

3. [Setup and Installation](#-setup-and-installation)

4. [Running the Notebook](#-running-the-notebook)

5. [Methodology](#️-methodology)

6. [Results](#-results)

7. [Key Takeaways](#-key-takeaways)

8. [Future Work](#-future-work)



## 🎯 Problem Statement



This project addresses the challenge of distinguishing between real and fake news articles using Natural Language Processing (NLP) techniques and machine learning algorithms. Our goal is to develop a classifier that can accurately identify fake news, contributing to the ongoing efforts to combat misinformation.



## 📊 Dataset



- **Source**: [Kaggle - Fake and Real News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)

- **File**: "Fake_Real_Data.csv"

- **Columns**: Text (news content), Label (Fake or Real)

- **Size**: 9,900 entries (5,000 Fake, 4,900 Real)



## 🛠 Setup and Installation



To run this project, you'll need Python and Jupyter Notebook installed. Follow these steps:



1. Ensure you have Jupyter Notebook installed. If not, you can install it using:

   ```

   pip install jupyter

   ```



2. Install the required packages. You can do this directly in a code cell within the notebook:

   ```python

   !pip install pandas numpy scikit-learn spacy

   ```



3. Download the spaCy model. Run this in a code cell:

   ```python

   !python -m spacy download en_core_web_lg

   ```



4. Ensure you have the "Fake_Real_Data.csv" file in the same directory as the notebook.



## 🚀 Running the Notebook



1. Start Jupyter Notebook:

   ```

   jupyter notebook

   ```



2. Open the "Fake_News_Classification.ipynb" file in the Jupyter interface.



3. Run the cells in order, following the instructions within the notebook.



## 🛠️ Methodology



The notebook guides you through the following steps:



### 1. Data Loading and Exploration



```python

import pandas as pd



# Read the dataset

df = pd.read_csv("Fake_Real_Data.csv")



# Print the shape of dataframe

print(df.shape)



# Print top 5 rows

df.head(5)



# Check the distribution of labels 

df['label'].value_counts()

```



### 2. Text Vectorization



We use spaCy's `en_core_web_lg` model to create word embeddings:



```python

import spacy

nlp = spacy.load("en_core_web_lg")



# This will take some time (nearly 15 minutes)

df['vector'] = df['Text'].apply(lambda text: nlp(text).vector)

```



### 3. Data Splitting



```python

from sklearn.model_selection import train_test_split



X_train, X_test, y_train, y_test = train_test_split(

    df.vector.values,

    df.label_num,

    test_size=0.2,

    random_state=2022

)



import numpy as np



X_train_2d = np.stack(X_train) # converting to 2d numpy array

X_test_2d = np.stack(X_test)

```



### 4. Model Training and Evaluation



We implement and compare two models:



#### Multinomial Naive Bayes



```python

from sklearn.naive_bayes import MultinomialNB

from sklearn.preprocessing import MinMaxScaler



scaler = MinMaxScaler()

scaled_train_embed = scaler.fit_transform(X_train_2d)

scaled_test_embed = scaler.transform(X_test_2d)



clf = MultinomialNB()

clf.fit(scaled_train_embed, y_train)



from sklearn.metrics import classification_report



y_pred = clf.predict(scaled_test_embed)

print(classification_report(y_test, y_pred))

```



#### K-Nearest Neighbors (KNN)



```python

from sklearn.neighbors import KNeighborsClassifier



clf = KNeighborsClassifier(n_neighbors=5, metric='euclidean')

clf.fit(X_train_2d, y_train)



y_pred = clf.predict(X_test_2d)

print(classification_report(y_test, y_pred))

```



## 📊 Results



The notebook presents classification reports for both models:



### Multinomial Naive Bayes

```

             precision    recall  f1-score   support



           0       0.95      0.94      0.95      1024

           1       0.94      0.95      0.94       956



    accuracy                           0.94      1980

   macro avg       0.94      0.94      0.94      1980

weighted avg       0.94      0.94      0.94      1980

```



### K-Nearest Neighbors (KNN)

```

            precision    recall  f1-score   support



           0       1.00      0.99      0.99      1024

           1       0.99      0.99      0.99       956



    accuracy                           0.99      1980

   macro avg       0.99      0.99      0.99      1980

weighted avg       0.99      0.99      0.99      1980

```



## 🔑 Key Takeaways



1. **Effective Vectorization**: GloVe embeddings from spaCy provided rich 300-dimensional vectors, capturing semantic relationships effectively.



2. **Model Performance**: 

   - KNN achieved exceptional accuracy (99%), benefiting from the compact, semantic-rich GloVe vectors.

   - Multinomial Naive Bayes performed well (94% accuracy) after scaling to handle negative values.



3. **Preprocessing Impact**: Pre-trained GloVe embeddings significantly enhanced both models' performance, especially KNN.



4. **Time Consideration**: While GloVe embedding is time-consuming (about 15 minutes for this dataset), it results in high-quality feature representations.



## 🚀 Future Work



1. Experiment with deep learning models like LSTM or BERT.

2. Incorporate additional features (e.g., article source, publication date).

3. Develop a real-time classification system.

4. Explore explainable AI techniques for model interpretability.



---



📌 **Note**: This project is for educational purposes. Always critically evaluate news sources and cross-reference information, regardless of model predictions.