Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning (original) (raw)

An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network

International Journal of Interactive Multimedia and Artificial Intelligence, 2023

Fake news is detrimental for society and individuals. Since the information dissipation through online media is too quick, an efficient system is needed to detect and counter the propagation of fake news on social media. Many studies have been performed in last few years to detect fake news on social media. This study focuses on efficient detection of fake news on social media, through a Natural Language Processing based approach, using deep learning. For the detection of fake news, textual data have been analyzed in unidirectional way using sequential neural networks, or in bi-directional way using transformer architectures like Bidirectional Encoder Representations from Transformers (BERT). This paper proposes Contextualized Fake News Detection System (ConFaDe)-a deep learning based fake news detection system that utilizes contextual embeddings generated from a transformer-based model. The model uses Masked Language Modelling and Replaced Token Detection in its pre-training to capture contextual and semantic information in the text. The proposed system outperforms the previously set benchmarks for fake news detection; including state-of-the-art approaches on a real-world fake news dataset, when evaluated using a set of standard performance metrics with an accuracy of 99.9 % and F1 macro of 99.9%. In contrast to the existing state-of-the-art model, the proposed system uses 90 percent less network parameters and is 75 percent lesser in size. Consequently, ConFaDe requires fewer hardware resources and less training time, and yet outperforms the existing fake news detection techniques, a step forward in the direction of Green Artificial Intelligence.

Implementation Of Deep Learning For Fake News Classification In Bahasa Indonesia

Journal Research of Social Science, Economics, and Management

Fake news has become a serious threat in the digital information era. This research aims to develop a model for detecting fake news in Bahasa Indonesia using a deep learning approach, combining the Long Short-Term Memory (LSTM) method with word representations from Word2vec Continuous Bag of Words (CBOW) to achieve optimal results. Our main model is LSTM, optimized through hyperparameter tuning. This model can process information sequentially from both directions, allowing for a better understanding of the news context. The integration of Word2vec CBOW enriches the model's understanding of word relationships in news text, enabling the identification of important patterns for news classification. The evaluation results show that our model performs very well in detecting fake news. After the tuning process, we achieved an F1-Score of 97.30% and an Accuracy of 98.38%. 10-fold cross-validation yielded even better results, with an F1-Score and Accuracy reaching 99%.

Fake news detection through deep learning techniques

International journal of health sciences

This paper aims to predict whether a given news article is real or fake. We use the dataset available on Kaggle. We then implement several deep learning models (Long short-term Memory (LSTM), Multi-layerPerceptron (MLP), Convolution Neural Networks (CNN), Hybrid CNN-LSTM on this dataset. For these models we examine the effects of character-based vs. word-based models and pretrained embeddings vs. learned embeddings. We also compare the accuracies of various models and report the best accuracy which we would get from a particular model.

Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

Computers, Materials & Continua, 2022

In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic. The initial feature extraction process is done using a convolutional recurrent neural network (CRNN). After the extraction of features, word indexing is done with high dimensionality. Then, based on the indexing measures, the ranking process identifies whether news is fake or real. The fuzzy CRNN model is trained to yield outstanding results with 99.99 ± 0.01% accuracy. This work utilizes three different datasets (LIAR, LIAR-PLUS, and ISOT) to find the most accurate model.

A Comprehensive Review on Fake News Detection with Deep Learning

IEEE Access

A protuberant issue of the present time is that, organizations from different domains are struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to distinguish fake information on the internet as it is often written to deceive users. Compared with many machine learning techniques, deep learning-based techniques are capable of detecting fake news more accurately. Previous review papers were based on data mining and machine learning techniques, scarcely exploring the deep learning techniques for fake news detection. However, emerging deep learning-based approaches such as Attention, Generative Adversarial Networks, and Bidirectional Encoder Representations for Transformers are absent from previous surveys. This study attempts to investigate advanced and state-ofthe-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. Then, we proceed with the discussion on the dataset used in previous research and their NLP techniques. A comprehensive overview of deep learning-based techniques has been bestowed to organize representative methods into various categories. The prominent evaluation metrics in fake news detection are also discussed. Nevertheless, we suggest further recommendations to improve fake news detection mechanisms in future research directions.

Fake news detection using deep learning models: A novel approach

Transactions on Emerging Telecommunications Technologies, 2019

With the ever increase in social media usage, it has become necessary to combat the spread of false information and decrease the reliance of information retrieval from such sources. Social platforms are under constant pressure to come up with efficient methods to solve this problem because users' interaction with fake and unreliable news leads to its spread at an individual level. This spreading of misinformation adversely affects the perception about an important activity, and as such, it needs to be dealt with using a modern approach. In this paper, we collect 1356 news instances from various users via Twitter and media sources such as PolitiFact and create several datasets for the real and the fake news stories. Our study compares multiple state‐of‐the‐art approaches such as convolutional neural networks (CNNs), long short‐term memories (LSTMs), ensemble methods, and attention mechanisms. We conclude that CNN + bidirectional LSTM ensembled network with attention mechanism ach...

Towards better representation learning using hybrid deep learning model for fake news detection

springer Link, 2022

Detection of Fake news articles over the internet is a difficult task due to huge amount of content being proliferated. Fake news proliferation is a major issue as it has socio-political impacts and it may change the opinion of the people. The easy dissemination of information through social media has added to exponential growth of fake news. Thus, it is challenging task to detect the fake news on the internet. In the literature, fake news detection techniques have been developed using machine learning approaches. Usually, fake news consists of sequential data. Recently, different variants of the Recurrent Neural Networks have been used for fake news detection due to better handling of sequential data and preserving better context information. Due to diversity in fake news data there is still need to develop the fake news detection techniques with better performance. In this work, we have developed a hybrid fake news detection model which aims at better representation learning to enhance the fake news detection performance. The proposed hybrid model has been developed using N-gram with TF-IDF to extract the content-based features then sequential features have been extracted using deep learning model [LSTM or bidirectional Encoder representation from transformers (BERT)]. The performance of the proposed approach has been evaluated using two publicly available datasets. It is observed from results that the proposed approach performs better the fake news detection approaches developed in the literature. The proposed approach has given the accuracies of 96.8% and 94% for the WELFAKE and KaggleFakeNews datasets, respectively.

A Deep Learning Based Approach for Fake News Detection

International Journal of Scientific Research in Science, Engineering and Technology, 2021

Owing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problemOwing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problem

Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection

arXiv (Cornell University), 2022

Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.

FAKE NEWS DETECTION USING DEEP LEARNING

Due to the exponential growth of information online, it is becoming impossible to decipher the true from the false. Thus, this leads to the problem of fake news. This research considers previous and current methods for fake news detection in textual formats while detailing how and why fake news exists in the first place. This paper includes a discussion on Linguistic Cue and Network Analysis approaches, and proposes a three-part method using Naïve Bayes Classifier, Support Vector Machines, and Semantic Analysis as an accurate way to detect fake news on social media. This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they're used in other articles in your dataset) can only get you so far. But these models do not consider the important qualities like word ordering and context. It is very possible that two articles that are similar in their word count will be completely different in their meaning. The data science community has responded by taking actions against the problem. There is a Kaggle competition called as the "Fake News Challenge" and Facebook is employing AI to filter fake news stories out of users' feeds. Combatting the fake news is a classic text classification project with a straight forward proposition. Is it possible for you to build a model that can differentiate between "Real "news and "Fake" news? So a proposed work on assembling a dataset of both fake and real news and employ a Naive Bayes classifier in order to create a model to classify an article into fake or real based on its words and phrases.