Deepfake Tweets Detection Using Deep Learning Algorithms

Kirn, Hina; Anwar, Muhammad; Sadiq, Ashina; Zeeshan, Hafiz M.; Mehmood, Imran; Butt, Rizwan Aslam

doi:10.3390/engproc2022020002

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The pre-trained language models such as GloVe, BERT, and Funnel were widely used for text representation due to the inclusion of word context and semantics into the representation. Various machine and deep learning techniques were also investigated [8,16,[25][26][27][28][29], such as linear super vector machine (LSVM) [15], Random Forest (RF) and decision tree [30], capsule neural networks [23], and convolutional neural network [31,32]. CNN-based classifiers were frequently used as it proves their effectiveness for text classification in different domains.…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique

Ali

Ghaleb

Al‐rimy

et al. 2022

Sensors

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Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

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Section: Introductionmentioning

confidence: 99%

Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique

Ali

Ghaleb

Al‐rimy

et al. 2022

Sensors

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“…Several survey and review papers that contain detection challenges and potential open issues have been published recently, including but not limited to [34][35][36][37][40][41][42][43]. Many issues have been researched related to feature extraction [6][7][8][9][10], representation [8,[11][12][13][14][15][16][17], classification [6,12,[18][19][20][21][22][23][24][25], and model design [10,16,21,22,[26][27][28][29][30][31][32]. Various solutions have been investigated using statical, traditional machine and deep learning and natural languageprocessing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate and validate the proposed fake news detection model, four commonly used evaluation measures, as utilized in similar studies [5,11,18,20,44], were employed. These measures include accuracy, precision, recall, and F-measure, which are calculated using the concepts of true positive (TP), true negative (TN), false positive (FP), and false negative (FN).…”

Section: Performance Measurementioning

confidence: 99%

“…Several approaches have been suggested to identify fake news. Numerous issues have been studied related to feature extraction [6][7][8][9][10], representation [8,[11][12][13][14][15][16][17], classification [6,12,[18][19][20][21][22][23][24][25], and model design [10,16,21,22,[26][27][28][29][30][31][32]. Based on the representative features that are employed, fake news detection techniques can be can be categorized into four groups: content-, knowledge-, users-, and propagation-based features.…”

Section: Introductionmentioning

confidence: 99%

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Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

et al. 2023

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Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

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