Fake news detection using deep learning models: A novel approach

Kumar, Sachin; Asthana, R. G. S.; Upadhyay, Shashwat; Upreti, Nidhi; Akbar, Mohammad

doi:10.1002/ett.3767

Cited by 136 publications

(85 citation statements)

References 23 publications

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“…3 depicts an interesting rumor campaign about "Saudi Arabia's first female robot citizen beheaded," which shows how Fake news Fake news is a modified version of an original news story which is spread intentionally and very difficult to identify (Cui et al 2019). It mimics traditional news and spreads easily on social media, reaches a large number of people quickly, and deceives many (Kumar et al 2019;Shu et al 2017).…”

Section: Types Of Misinformationmentioning

confidence: 99%

“…For example, Chen et al (2017) introduced a convolutional neural network-based classification method with single and multi-word embedding for identifying both rumor and stance tweets. Kumar et al (2019) introduced both a CNN and a bidirectional LSTM ensembled network with an attention mechanism to solve MID. Additionally, Yang et al (2018) stated that online social media is continually growing in popularity and genuine users are being attacked by many fraudulent users.…”

Section: Discriminative Model For Detecting Misinformationmentioning

confidence: 99%

“…Kumar et al. ( 2019 ) introduced both a CNN and a bidirectional LSTM ensembled network with an attention mechanism to solve MID. Additionally, Yang et al.…”

Section: Deep Learning For Misinformation Detection: State-of-the-artmentioning

confidence: 99%

“…It mimics traditional news and spreads easily on social media, reaches a large number of people quickly, and deceives many (Kumar et al. 2019 ; Shu et al. 2017 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Types Of Misinformationmentioning

confidence: 99%

Section: Discriminative Model For Detecting Misinformationmentioning

confidence: 99%

“…Kumar et al. ( 2019 ) introduced both a CNN and a bidirectional LSTM ensembled network with an attention mechanism to solve MID. Additionally, Yang et al.…”

Section: Deep Learning For Misinformation Detection: State-of-the-artmentioning

confidence: 99%

“…It mimics traditional news and spreads easily on social media, reaches a large number of people quickly, and deceives many (Kumar et al. 2019 ; Shu et al. 2017 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

195

View full text Add to dashboard Cite

show abstract

“…Kaliyar et al [23] created a CNN-based deep neural network called FNDNet and achieved state-of-the-art results with an accuracy of 98.36% on Kaggle fake news dataset. Kumar et al [25] performed a CNN + BiLSTM ensembled model with attention mechanism on their own datasets and FakeNewsNet dataset and achieved the highest accuracy of 88.78%. Ajao et al [4] used a hybrid of CNN and RNN to classify fake news messages from Twitter posts.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Stacking Approach for Accurate Detection of Fake News

et al. 2021

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With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news are so similar to the real ones that it is difficult for human to identify them. Therefore, automated fake news detection tools like machine learning and deep learning models have become an essential requirement. In this paper, we evaluated the performance of five machine learning models and three deep learning models on two fake and real news datasets of different size with hold out cross validation. We also used term frequency, term frequencyinverse document frequency and embedding techniques to obtain text representation for machine learning and deep learning models respectively. To evaluate models' performance, we used accuracy, precision, recall and F1-score as the evaluation metrics and a corrected version of McNemar's test to determine if models' performance is significantly different. Then, we proposed our novel stacking model which achieved testing accuracy of 99.94% and 96.05 % respectively on the ISOT dataset and KDnugget dataset. Furthermore, the performance of our proposed method is high as compared to baseline methods. Thus, we highly recommend it for fake news detection.

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Deep learning recognition of diseased and normal cell representation

Iqbal

Ahmad

Luo

et al. 2020

Trans Emerging Tel Tech

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Cell classification refers to detecting normal and diseased cells from small amount of data. Sometimes, classification of cells becomes difficult because some cells fall into more than one categories/classes. Current state‐of‐the‐art cell classification methods have been developed on the bases of tumor cell classification but these methods cannot classify diseased or normal cells. This study investigated the performance of two classification methods traditional machine learning and deep learning (normal and diseased cell classification) to categorize normal and diseased cells. Millions of normal cells undergo controlled growth and uncontrolled growth may be involved in disease causation but their clinical applications remain limited due to difficulties in distinguishing normal and diseased cells. Previous studies are limited to identify, systematically, the normal and diseased cells. This study collected information about diseased or normal cells to check the networks for correct cell detection and then eliminated false‐positive cells. We used machine learning methods along with logistic regression, support vector machine, and CNN (convolutional neural network). We found that our proposed method classified better the normal and diseased cells. With the help of two types of images: normal and diseased cells, we trained a CNN that identified diseased cells with 98% accuracy and enabled the discovery of normal and diseased cells. As a result, it will advance the clinical utility of human diseased cells.

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Fake news detection using deep learning models: A novel approach

Cited by 136 publications

References 23 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

A Novel Stacking Approach for Accurate Detection of Fake News

Deep learning recognition of diseased and normal cell representation

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