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How to Detect Fake News using Python

Jul 13, 2020 DataScience, Python, SubhamRay, 5359 Views
DataScience | Python | Detect Fake News

Detecting Fake News

In this article we will cover :

  • What is Python?
  • What is Fake News?
  • The source code.
  • Resources.

What is the Python Programming Language?

Python Programming language is an interpreted, object-oriented, high-level programming language with dynamic semantics, supporting modules and packages, which encourages program modularity and code reuse. It has the ability to create CSV output for easy data reading in a spreadsheet which alternatively more complicated file outputs that can be ingested by machine learning clusters for computation.

What is Fake News?

Fake News is Junk news or pseudo News, which usually contains disinformation, intended for misleading information for a particular topic that may contain fabricated headlines to increase readership.

The Source Code:

  • Install libraries with pip
    pip install numpy pandas sklearn​
  • First import the important imports:
    import numpy as np
    import pandas as pd
    import itertools​
  • After that import from sklearn
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score, confusion_matrix
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import PassiveAggressiveClassifier​
  • Read the data from the dataset
    #Read the data
    df=pd.read_csv('C:\\DataSet\\newsdataset.csv')
    ​
  • Shape and read your dataset and understand
    df.shape
    df.head(10)​
  • Get the labels from DataFrame from the dataset
    labels=df.label
    labels.head()​
  • Split Dataset into training and testing sets
    x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)​
  • fit and transform the vectorizer on the train set, and transform the vectorizer
    tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)
    
    tfidf_train=tfidf_vectorizer.fit_transform(x_train) 
    tfidf_test=tfidf_vectorizer.transform(x_test)​
  • Initialize the Passive-Aggressive Classifier
    pac=PassiveAggressiveClassifier(max_iter=50)
    pac.fit(tfidf_train,y_train)
    y_pred=pac.predict(tfidf_test)
    score=accuracy_score(y_test,y_pred)
    print(f'Accuracy: {round(score*100,2)}%')​
  • Apply Confusion Matrix to gain insights
    confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])​

Resources

  • https://www.python.org/
  • https://datascience.berkeley.edu/about/what-is-data-science
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