Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task

Gautam, Akansha; Venktesh, V.; Masud, Sarah

doi:10.1007/978-3-030-73696-5_18

Cited by 33 publications

(16 citation statements)

References 15 publications

(13 reference statements)

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“…This indicates that the model was successful in distinguishing fake tweets/News about the COVID-19 disease outbreak. Decision Tree [38] 85.39 85.37 Gradient Boost [38] 86.96 86.96 Logistic Regression [38] 91.96 91.96 Support Vector Machine [38] 93.32 93.32 (Baseline) XLNet + LDA [31] 96.70 96.60 Ensemble [32] 94.00 93.90 CT-BERT + hard voting [33] 98…”

Section: Performance Comparison: Proposed Model Versus Ensemble Deep ...mentioning

confidence: 99%

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news

Malla

Alphonse

2022

Eur. Phys. J. Spec. Top.

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The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model’s performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models’ incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score.

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Section: Performance Comparison: Proposed Model Versus Ensemble Deep ...mentioning

confidence: 99%

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news

Malla

Alphonse

2022

Eur. Phys. J. Spec. Top.

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“…Another Transformer used for Fake News detection is XLNet [48], an extension of BERT. In [13], the XLNet model is combined with topic information and applied on a relatively small annotated dataset (∼10K).…”

Section: Related Workmentioning

confidence: 99%

“…8, 9, and 10 present the comparison between Generic and Specific word Embeddings for each employed model when using Lemma Text Preprocessing. Figures 11,12,and 13 present the comparison between Generic and Specific word Embeddings for each employed model when using Aggressive Text Preprocessing.…”

Section: Recallmentioning

confidence: 99%

Context-Aware Misinformation Detection: A Benchmark of Deep Learning Architectures Using Word Embeddings

Ilie

Truică²,

Apostol

et al. 2021

IEEE Access

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New mass media paradigms for information distribution have emerged with the digital age. With new digital-enabled mass media, the communication process is centered around the user, while multimedia content is the new identity of news. Thus, the media landscape has shifted from mass media to personalized social media. While this progress brings advantages, it also carries the risk of being detrimental to society through the emergence of misinformation (false or inaccurate information) and disinformation (intentionally spreading misinformation) in the form of fake news. Fake news is a tool used to manipulate public opinion on particular topics, distort public perceptions, and generate social unrest while lacking the rigor of traditional journalism. Driven by this current and real-world problem, in this paper, we train multiple Deep Learning architectures for multi-class classification and compare their performance in detecting the veracity of the news articles. To achieve accurate models in detecting misinformation, we employ a large dataset containing 100 000 news articles labeled with ten classes (one with real news and the rest with different types of fake news). We use two preprocessing techniques, i.e., one simple and another very aggressive, to clean the dataset. We also employ three word embeddings that preserve the word context, i.e., Word2Vec, FastText, and GloVe, pre-trained and trained on our dataset to vectorize the preprocessed dataset. For the misinformation task, we train a Logistic Regression as a baseline and compare its results with the performance of ten Deep Learning architectures. We obtain the best results using a Recurrent Convolutional Neural Network based architecture. The experimental results show that the models are highly dependable on text preprocessing and the word embedding employed.

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“…The authors improved the results using models ensemble, which allowed them to obtain the maximum f1-score of 0.9831 for the method RoBERTa?XLM-RoBERTa?XLNet?-DeBERT. Another paper which concerned the detection of fake news related to the COVID-19 infodemics was [11], in which the XLNet with topic distribution method was proposed. The proposed method obtained an f1-score of 0.967, which exceeded all the other compared methods (USE ?…”

Section: Introductionmentioning

confidence: 99%

Implementation of the BERT-derived architectures to tackle disinformation challenges

Kula

Kozik

Choraś

2021

Neural Comput & Applic

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Recent progress in the area of modern technologies confirms that information is not only a commodity but can also become a tool for competition and rivalry among governments and corporations, or can be applied by ill-willed people to use it in their hate speech practices. The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools. The design of models meeting the above-mentioned criteria is enabled by artificial intelligence and, above all, by the state-of-the-art neural network architectures, applied in NLP tasks. The BERT neural network belongs to this type of architectures. This paper presents Transformer-based hybrid architectures applied to create models for detecting fake news.

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Fake News Detection System Using XLNet Model with Topic Distributions: CONSTRAINT@AAAI2021 Shared Task

Cited by 33 publications

References 15 publications

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news

Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news

Context-Aware Misinformation Detection: A Benchmark of Deep Learning Architectures Using Word Embeddings

Implementation of the BERT-derived architectures to tackle disinformation challenges

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