Fake News Detection Model using TensorFlow in Python Last Updated : 21 Mar, 2025 Comments Improve Suggest changes Like Article Like Report Fake news is a type of misinformation that can mislead readers, influence public opinion, and even damage reputations. Detecting fake news prevents its spread and protects individuals and organizations. Media outlets often use these models to help filter and verify content, ensuring that the news shared with the public is accurate. In this article we'll build a deep learning model using TensorFlow in Python to detect fake news from text.Implementation of Fake News Detection ModelWe will be building the model with following steps to make our model:1. Importing LibrariesThe libraries we will be using are numpy, pandas, scikit learn and tenserflow Python import numpy as np import pandas as pd import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn import preprocessing from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences 2. Importing the DatasetWe will be using fake news dataset, which contains News text and corresponding label (FAKE or REAL). Dataset can be downloaded from this link. Python data = pd.read_csv("news.csv") data.head() Output: Dataset3. Preprocessing DatasetAs we can see the dataset contains one unnamed column. So we drop that column from the dataset. Python data = data.drop(["Unnamed: 0"], axis=1) data.head(5) Output:Cleaned DatasetNow that data is cleaned we can go for data encoding.4. Data EncodingIt converts the categorical column (label in out case) into numerical values.le.fit(data['label']): Fits the encoder on the 'label' column to learn the unique categories.data['label'] = le.transform(data['label']): Transforms the categorical labels into numerical format (0 for REAL, 1 for FAKE). Python le = preprocessing.LabelEncoder() le.fit(data['label']) data['label'] = le.transform(data['label']) 5. Variables SetupThese are some variables required to be setup for the model training. Python embedding_dim = 50 max_length = 54 padding_type = 'post' trunc_type = 'post' oov_tok = "<OOV>" training_size = 3000 test_portion = 0.1 6. Tokenization This process divides a large piece of continuous text into distinct units or tokens. Here we use columns separately for a temporal basis as a pipeline just for good accuracy.tokenizer1.fit_on_texts(title): Fits the tokenizer on the 'title' column to create a vocabulary.pad_sequences(sequences1): Pads the sequences to ensure they all have the same length. Python title = [] text = [] labels = [] for x in range(training_size): title.append(data['title'][x]) text.append(data['text'][x]) labels.append(data['label'][x]) tokenizer1 = Tokenizer() tokenizer1.fit_on_texts(title) word_index1 = tokenizer1.word_index vocab_size1 = len(word_index1) sequences1 = tokenizer1.texts_to_sequences(title) padded1 = pad_sequences(sequences1, padding=padding_type, truncating=trunc_type) 7. Splitting Data for Training and Testingtraining_sequences1, test_sequences1: Splits the tokenized and padded data into training and testing sets.training_labels, test_labels: Splits the corresponding labels into training and testing labels. Python split = int(test_portion * training_size) training_sequences1 = padded1[split:training_size] test_sequences1 = padded1[0:split] test_labels = labels[0:split] training_labels = labels[split:training_size] 8. Reshaping Data for LSTMWe will be using LSTM(Long Short Term Memory) model for prediction and for that we need to reshape padded sequence. We are converting it into np.array() as we need training and test sequences into NumPy arrays which are required by TensorFlow models. Python training_sequences1 = np.array(training_sequences1) test_sequences1 = np.array(test_sequences1) 9. Generating Word EmbeddingEmbeddings allows words with similar meanings to have a similar representation. Here each individual word is represented as real-valued vectors in a predefined vector space. For that we will be using glove.6B.50d.txt.!wget: Downloads the pre-trained GloVe embeddings from the following link.!unzip: Unzips the downloaded file containing the GloVe embeddings. Python !wget http://nlp.stanford.edu/data/glove.6B.zip !unzip glove.6B.zip Output:Glove EmbeddingsNow that our glove embeddings are downloaded we can use them for word embedding. Python embedding_index = {} with open('glove.6B.50d.txt', 'r', encoding='utf-8') as f: for line in f: values = line.split() word = values[0] coefs = np.asarray(values[1:], dtype='float32') embedding_index[word] = coefs embedding_matrix = np.zeros((vocab_size1 + 1, embedding_dim)) for word, i in word_index1.items(): if i < vocab_size1: embedding_vector = embedding_index.get(word) if embedding_vector is not None: embedding_matrix[i] = embedding_vector 10. Model ArchitectureHere we use the TensorFlow embedding technique with Keras Embedding Layer where we map original input data into some set of real-valued dimensions.Embedding: The embedding layer uses pre-trained GloVe embeddings.Conv1D: A 1D convolutional layer to detect patterns in the text.LSTM(64): An LSTM layer to capture long-term dependencies in the data. Python model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size1 + 1, embedding_dim, input_length=max_length, weights=[embedding_matrix], trainable=False), tf.keras.layers.Dropout(0.2), tf.keras.layers.Conv1D(64, 5, activation='relu'), tf.keras.layers.MaxPooling1D(pool_size=4), tf.keras.layers.LSTM(64), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() Output : Model SummaryNow that our model architecture is ready we can use this to train our model.11. Training the Model Python history = model.fit( training_sequences1, np.array(training_labels), epochs=50, validation_data=(test_sequences1, np.array(test_labels)), verbose=2 ) Output: Trained modelFor each epoch training accuracy improves reaching around 97% by the 50th epoch while the validation accuracy is around 75%. The validation loss gradually decreases, indicating that the model is learning from the data but it also shows signs of some overfitting as the validation accuracy is lower than the training accuracy. To avoid this we can further fine tune the model.12. Sample PredictionWe will test model accuracy with a sample of text to see how our model is working. Python X = "Karry to go to France in gesture of sympathy" sequences = tokenizer1.texts_to_sequences([X]) sequences = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type) if model.predict(sequences, verbose=0)[0][0] >= 0.5: print("This news is True") else: print("This news is False") Output: This news is FalseAs we can see our model is working fine and now can be used to detect of any information is fake or not.By following these steps we successfully built a fake news detection model using TensorFlow in Python. This model can be further improved by fine-tuning the hyperparameters, trying different architectures or using more advanced techniques like attention mechanisms. In real-world applications such models can be integrated into news websites or social media platforms to automatically flag fake news. 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It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require 2 min read Voice Assistant using pythonSpeech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required 3 min read Human Activity Recognition - Using Deep Learning ModelHuman activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. 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In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews 5 min read Building an Autocorrector Using NLP in PythonAutocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N 4 min read Python | NLP analysis of Restaurant reviewsNatural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach 7 min read Restaurant Review Analysis Using NLP and SQLiteNormally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei 9 min read Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini 10 min read Clustering ProjectsCustomer Segmentation using Unsupervised Machine Learning in PythonCustomer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning 5 min read Music Recommendation System Using Machine LearningWhen did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona 4 min read K means Clustering – IntroductionK-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ 4 min read Image Segmentation using K Means ClusteringImage segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image. Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im 2 min read Recommender System ProjectAI Driven Snake Game using Deep Q LearningContent has been removed from this Article 1 min read Like