Fake news detection using deep learning models: A novel approach

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

Computation ◽

10.3390/computation9020020 ◽

2021 ◽

Vol 9 (2) ◽

pp. 20

Author(s):

Despoina Mouratidis ◽

Maria Nefeli Nikiforos ◽

Katia Lida Kermanidis

Keyword(s):

Deep Learning ◽

Online Information ◽

Fake News ◽

Neural Network Learning ◽

Text Input ◽

The Past ◽

Network Layers ◽

Novel Approach ◽

Rapid Spread ◽

Accuracy Performance

In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.

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Adaptive Model Scheduling for Resource-efficient Data Labeling

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3494559 ◽

2022 ◽

Vol 16 (4) ◽

pp. 1-22

Author(s):

Mu Yuan ◽

Lan Zhang ◽

Xiang-Yang Li ◽

Lin-Zhuo Yang ◽

Hui Xiong

Keyword(s):

Deep Learning ◽

Heuristic Algorithms ◽

Data Item ◽

Adaptive Model ◽

Learning Models ◽

The People ◽

Novel Approach ◽

Machine Learning Model ◽

Efficient Data ◽

Model Execution

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.

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An Exploration of Machine and Deep Learning Models for Fake News Detection in Social Media

10.1109/besc53957.2021.9635201 ◽

2021 ◽

Author(s):

Yi Tian ◽

Jialing Gu ◽

Yiiun Jia ◽

Richard O. Sinnott

Keyword(s):

Social Media ◽

Deep Learning ◽

Fake News ◽

Learning Models

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A Novel Approach Towards Fake News Detection: Deep Learning Augmented with Textual Entailment Features

Natural Language Processing and Information Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-23281-8_30 ◽

2019 ◽

pp. 345-358 ◽

Cited By ~ 6

Author(s):

Tanik Saikh ◽

Amit Anand ◽

Asif Ekbal ◽

Pushpak Bhattacharyya

Keyword(s):

Deep Learning ◽

Fake News ◽

Novel Approach ◽

Textual Entailment

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Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Complexity ◽

10.1155/2021/5557784 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Nida Aslam ◽

Irfan Ullah Khan ◽

Farah Salem Alotaibi ◽

Lama Abdulaziz Aldaej ◽

Asma Khaled Aldubaikil

Keyword(s):

Social Media ◽

Deep Learning ◽

Learning Model ◽

Experimental Results ◽

Ensemble Model ◽

Fake News ◽

Learning Models ◽

Social Media Networks ◽

Deep Learning Model

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

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Attention-Based Deep Learning Models for Detection of Fake News in Social Networks

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.295809 ◽

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Deep Learning ◽

Deception Detection ◽

Detection System ◽

Network Models ◽

Complex Problem ◽

Learning Approaches ◽

Fake News ◽

Learning Models ◽

Neural Network Models ◽

Deep Learning Model

Automatic fake news detection is a challenging problem in deception detection. While evaluating the performance of deep learning-based models, if all the models are giving higher accuracy on a test dataset, it will make it harder to validate the performance of the deep learning models under consideration. So, we will need a complex problem to validate the performance of a deep learning model. LIAR is one such complex, much resent, labeled benchmark dataset which is publicly available for doing research on fake news detection to model statistical and machine learning approaches to combating fake news. In this work, a novel fake news detection system is implemented using Deep Neural Network models such as CNN, LSTM, BiLSTM, and the performance of their attention mechanism is evaluated by analyzing their performance in terms of Accuracy, Precision, Recall, and F1-score with training, validation and test datasets of LIAR.

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