Detecting Fake News with Python and Machine Learning

Do you trust all the news you hear from social media?

All news are not real, right?

How will you detect fake news?

The answer is Python. By practicing this advanced python project of detecting fake news, you will easily make a difference between real and fake news.

Before moving ahead in this machine learning project, get aware of the terms related to it like fake news, tfidfvectorizer, PassiveAggressive Classifier.

Also, I like to add that DataFlair has published a series of machine learning Projects where you will get interesting and open-source advanced ml projects. Do check, and then share your experience through comments. Here is the list of top Python projects:

  1. Fake News Detection Python Project
  2. Parkinson’s Disease Detection Python Project
  3. Color Detection Python Project
  4. Speech Emotion Recognition Python Project 
  5. Breast Cancer Classification Python Project
  6. Age and Gender Detection Python Project 
  7. Handwritten Digit Recognition Python Project
  8. Chatbot Python Project
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What is Fake News?

A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end up being viralized by algorithms, and users may end up in a filter bubble.

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What is a TfidfVectorizer?

TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.

IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.

The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.

What is a PassiveAggressiveClassifier?

Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector.

Detecting Fake News with Python

To build a model to accurately classify a piece of news as REAL or FAKE.

About Detecting Fake News with Python

This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.

The fake news Dataset

The dataset we’ll use for this python project- we’ll call it news.csv. This dataset has a shape of 7796×4. The first column identifies the news, the second and third are the title and text, and the fourth column has labels denoting whether the news is REAL or FAKE. The dataset takes up 29.2MB of space and you can download it here.

Project Prerequisites

You’ll need to install the following libraries with pip:

pip install numpy pandas sklearn

You’ll need to install Jupyter Lab to run your code. Get to your command prompt and run the following command:

C:\Users\DataFlair>jupyter lab

You’ll see a new browser window open up; create a new console and use it to run your code. To run multiple lines of code at once, press Shift+Enter.

Steps for detecting fake news with Python

Follow the below steps for detecting fake news and complete your first advanced Python Project –

  1. Make necessary imports:
import numpy as np
import pandas as pd
import itertools
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.metrics import accuracy_score, confusion_matrix


importing data sets in python open source projects

2. Now, let’s read the data into a DataFrame, and get the shape of the data and the first 5 records.

#Read the data

#Get shape and head

Output Screenshot:

interesting python projects - read data frame

3. And get the labels from the DataFrame.

#DataFlair - Get the labels

Output Screenshot:

Python projects examples - get labels

4. Split the dataset into training and testing sets.

#DataFlair - Split the dataset
x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)


Python data science projects - split data sets

5. Let’s initialize a TfidfVectorizer with stop words from the English language and a maximum document frequency of 0.7 (terms with a higher document frequency will be discarded). Stop words are the most common words in a language that are to be filtered out before processing the natural language data. And a TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features.

Now, fit and transform the vectorizer on the train set, and transform the vectorizer on the test set.

#DataFlair - Initialize a TfidfVectorizer
tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)

#DataFlair - Fit and transform train set, transform test set


python data science projects

6. Next, we’ll initialize a PassiveAggressiveClassifier. This is. We’ll fit this on tfidf_train and y_train.

Then, we’ll predict on the test set from the TfidfVectorizer and calculate the accuracy with accuracy_score() from sklearn.metrics.

#DataFlair - Initialize a PassiveAggressiveClassifier

#DataFlair - Predict on the test set and calculate accuracy
print(f'Accuracy: {round(score*100,2)}%')

Output Screenshot:

python machine learning projects

7. We got an accuracy of 92.82% with this model. Finally, let’s print out a confusion matrix to gain insight into the number of false and true negatives and positives.

#DataFlair - Build confusion matrix
confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])

Output Screenshot:

python projects - confusion matrix

So with this model, we have 589 true positives, 587 true negatives, 42 false positives, and 49 false negatives.


Today, we learned to detect fake news with Python. We took a political dataset, implemented a TfidfVectorizer, initialized a PassiveAggressiveClassifier, and fit our model. We ended up obtaining an accuracy of 92.82% in magnitude.

Hope you enjoyed the fake news detection python project. Keep visiting DataFlair for more interesting python, data science, and machine learning projects.

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129 Responses

  1. Vibha Shukla says:

    I am getting error in the following part of the code
    #DataFlair – Fit and transform train set, transform test set

    part of error message – 2 tfidf_train=tfidf_vectorizer.fit_transform(x_train)

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_extraction\ in fit_transform(self, raw_documents, y)
    1379 Tf-idf-weighted document-term matrix.
    1380 “””
    -> 1381 X = super(TfidfVectorizer, self).fit_transform(raw_documents)
    1383 # X is already a transformed view of raw_documents so

    • DataFlair Team says:

      Assuming your tokenizer works as expected, I see two sections to look into. First, TfIdfVectorizer expects a list of strings. Second, max_df is the threshold to remove terms that appear too frequently. set it to 0.7 means it ignores terms that appear in more than 70% of the documents. Have a look at these 2 sectors, it will surely resolve your error.

  2. sofiane says:

    hello Gents,
    please where to ger the corresponding .csv file

  3. sarah says:

    where is the sourcecode?

    • DataFlair Team says:

      The source code is given in the article step wise with the explanation. Please copy the code and paste in Jupyter Notebook or Google Colab and execute accordingly.

  4. Nashra khan says:

    can you share the screenshot of your error

  5. Bhanu says:

    Hello, When I run the line ‘y_pred=pac.predict(tfidf_test)’, I get this error ‘X has 33518 features per sample; expecting 61651’ . Could not understand what’s wrong here. Can someone help me with this?
    Thank you in advance.

  6. farshid says:

    hi, how can i test this model in real world for example in New York Time’s articles ?

  7. Philip says:

    it worked well though i made some changes in parsing some codes but i followed the same approach thanks alot

  8. Jessica says:

    I get this error in step 6. How to fix?
    ValueError: Input contains NaN

  9. Sai teja says:

    Send me documentation of this project

  10. Merbazkhan says:

    I don’t understand how to rectify the same error issue said as above ,
    x_train not defined , apparently passing the fit and train model using tfidclassifier
    If anybody can do it ,

  11. David says:

    What date range is the data frome?

  12. Hex says:

    Is the test set still available? The link seems to be broken.

  13. Bilal Najib says:

    Can someone plz help me i cannot find the source code for this model

  14. David Tian says:

    Hi following up on my prior comment – can anyone provide a date range of the underlying data source?


  15. Shnouli Mukhherjee says:

    I can not understand the initialization and training of the set using TfidfVectorizer, can someone help me to understand the 5th block of code given in the article? also when I am implementing it, I am having a value error i.e
    ValueError: np.nan is an invalid document, expected byte or unicode string.

  16. krutika says:

    advanced features

  17. Felipe says:

    How can i use in real word?


  18. Mansi Rajput says:

    I m working this project …..Plz send me the source code of this project plz its urgent.

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