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Fake News Detection using Machine Learning

Fake News Detection using Machine Learning

Last Updated : 10 Sep, 2024

Fake news on different platforms is spreading widely and is a matter of serious concern, as it causes social wars and permanent breakage of the bonds established among people. A lot of research is already going on focused on the classification of fake news.

Here we will try to solve this issue with the help of machine learning in Python.

Before starting the code, download the dataset by clicking the link.

Steps to be followed

Importing Libraries and Datasets
Data Preprocessing
Preprocessing and analysis of News column
Converting text into Vectors
Model training, Evaluation, and Prediction

Importing Libraries and Datasets

The libraries used are :

Pandas: For importing the dataset.
Seaborn/Matplotlib: For data visualization.

Python

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt

Let's import the downloaded dataset.

Python

data = pd.read_csv('News.csv',index_col=0) data.head()

Output :

Data preprocessing

The shape of the dataset can be found by the below code.

Python

data.shape

Output:

(44919, 5)

As the title, subject and date column will not going to be helpful in identification of the news. So, we can drop these column.

Python

data = data.drop(["title", "subject","date"], axis = 1)

Now, we have to check if there is any null value (we will drop those rows)

Python

data.isnull().sum()

Output:

text     0
class    0

So there is no null value.

Now we have to shuffle the dataset to prevent the model to get bias. After that we will reset the index and then drop it. Because index column is not useful to us.

Python

# Shuffling data = data.sample(frac=1) data.reset_index(inplace=True) data.drop(["index"], axis=1, inplace=True)

Now Let's explore the unique values in the each category using below code.

Python

sns.countplot(data=data, x='class', order=data['class'].value_counts().index)

Output:

unique values in the each category

Preprocessing and analysis of News column

Firstly we will remove all the stopwords, punctuations and any irrelevant spaces from the text. For that NLTK Library is required and some of it's module need to be downloaded. So, for that run the below code.

Python

from tqdm import tqdm import re import nltk nltk.download('punkt') nltk.download('stopwords') from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem.porter import PorterStemmer from wordcloud import WordCloud

Once we have all the required modules, we can create a function name preprocess text. This function will preprocess all the data given as input.

Python

def preprocess_text(text_data):     preprocessed_text = []          for sentence in tqdm(text_data):         sentence = re.sub(r'[^\w\s]', '', sentence)         preprocessed_text.append(' '.join(token.lower()                                   for token in str(sentence).split()                                   if token not in stopwords.words('english')))      return preprocessed_text

To implement the function in all the news in the text column, run the below command.

Python

preprocessed_review = preprocess_text(data['text'].values) data['text'] = preprocessed_review

This command will take some time (as the dataset taken is very large).

Let's visualize the WordCloud for fake and real news separately.

Python

# Real consolidated = ' '.join(     word for word in data['text'][data['class'] == 1].astype(str)) wordCloud = WordCloud(width=1600,                       height=800,                       random_state=21,                       max_font_size=110,                       collocations=False) plt.figure(figsize=(15, 10)) plt.imshow(wordCloud.generate(consolidated), interpolation='bilinear') plt.axis('off') plt.show()

Output :

WordCloud for real news

Python

# Fake consolidated = ' '.join(     word for word in data['text'][data['class'] == 0].astype(str)) wordCloud = WordCloud(width=1600,                       height=800,                       random_state=21,                       max_font_size=110,                       collocations=False) plt.figure(figsize=(15, 10)) plt.imshow(wordCloud.generate(consolidated), interpolation='bilinear') plt.axis('off') plt.show()

Output :

Fake News Detection using Machine Learning - word cloud — WordCloud for fake news

Now, Let's plot the bargraph of the top 20 most frequent words.

Python

from sklearn.feature_extraction.text import CountVectorizer   def get_top_n_words(corpus, n=None):     vec = CountVectorizer().fit(corpus)     bag_of_words = vec.transform(corpus)     sum_words = bag_of_words.sum(axis=0)     words_freq = [(word, sum_words[0, idx])                   for word, idx in vec.vocabulary_.items()]     words_freq = sorted(words_freq, key=lambda x: x[1],                         reverse=True)     return words_freq[:n]   common_words = get_top_n_words(data['text'], 20) df1 = pd.DataFrame(common_words, columns=['Review', 'count'])  df1.groupby('Review').sum()['count'].sort_values(ascending=False).plot(     kind='bar',     figsize=(10, 6),     xlabel="Top Words",     ylabel="Count",     title="Bar Chart of Top Words Frequency" )

Output :

bargraph of the top 20 most frequent words

Converting text into Vectors

Before converting the data into vectors, split it into train and test.

Python

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression  x_train, x_test, y_train, y_test = train_test_split(data['text'],                                                      data['class'],                                                      test_size=0.25)

Now we can convert the training data into vectors using TfidfVectorizer.

Python

from sklearn.feature_extraction.text import TfidfVectorizer  vectorization = TfidfVectorizer() x_train = vectorization.fit_transform(x_train) x_test = vectorization.transform(x_test)

Model training, Evaluation, and Prediction

Now, the dataset is ready to train the model.

For training we will use Logistic Regression and evaluate the prediction accuracy using accuracy_score.

Python

from sklearn.linear_model import LogisticRegression  model = LogisticRegression() model.fit(x_train, y_train)  # testing the model print(accuracy_score(y_train, model.predict(x_train))) print(accuracy_score(y_test, model.predict(x_test)))

Output :

0.993766511324171
0.9893143365983972

Let's train with Decision Tree Classifier.

Python

from sklearn.tree import DecisionTreeClassifier  model = DecisionTreeClassifier() model.fit(x_train, y_train)  # testing the model print(accuracy_score(y_train, model.predict(x_train))) print(accuracy_score(y_test, model.predict(x_test)))

Output :

0.9999703167205913
0.9951914514692787

The confusion matrix for Decision Tree Classifier can be implemented with the code below.

Python

# Confusion matrix of Results from Decision Tree classification from sklearn import metrics cm = metrics.confusion_matrix(y_test, model.predict(x_test))  cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=cm,                                             display_labels=[False, True])  cm_display.plot() plt.show()

Output :

confusion matrix

Get the complete notebook link here:

Colab Link : click here.
Dataset Link : click here.

Conclusion

Decision Tree Classifier and Logistic regression are performing well.

Fake News Detection using Machine Learning

D

deepakshi_mahajan

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