Fake News Detection Using XLNet Fine-Tuning Model

J, Ashok Kumar; Trueman, Tina Esther; Cambria, Erik

doi:10.1109/iccica52458.2021.9697269

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…(2) XLNet/BERT (Devlin et al, 2018;Yang et al, 2019;Chintalapudi et al, 2021;Kumar et al, 2021): Using BERT or XLNet as popular transformer architecture models to extract features from text along with the fully connected network as a classifier.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

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Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Reisinezhad,

Fakhrahmad

2023

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PurposeQuestionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.Design/methodology/approachIn this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.FindingsLabeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.Research limitations/implicationsOur research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.Practical implicationsCombining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.Originality/valueTo the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

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Section: Evaluation and Resultsmentioning

confidence: 99%

“…In another paper, the XLNet model was used to detect fake news which is a problem in social networks. In this article, the XLNet model was fine-tuned to predict the appropriate class (fake or real) and it performed better than other older models (Kumar et al. , 2021).…”

Section: Related Workmentioning

confidence: 99%

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Reisinezhad,

Fakhrahmad

2023

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“…As a result, in this paper, we aim to take advantage of information derived from user friendships and to demonstrate their importance in the problem of detecting fake news. In most cases, standard tensor derivatization or decomposition methods are performed in an unsupervised environment [ 17 ]. The class information available for some of the data does not affect them.…”

Section: Introductionmentioning

confidence: 99%

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Xiao-yu

2022

Computational Intelligence and Neuroscience

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Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.

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“…The general conceptual model of fake news twitters detection. Data collection is the first step where twitter messages (tweets) are collected and saved as one database [2]. This dataset goes through several processing steps and analysis to detect fake news that may be provided inside tweets.…”

Section: Introductionmentioning

confidence: 99%

Research on the Ability to Detect Fake News with Machine Learning

Sahu¹,

Sharma²

2022

IJETMS

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The task of classifying news manually requires in-depth knowledge of the domain and expertise to spot anomalies in the text. During this research, we discussed the matter of classifying fake news articles using machine learning models and ensemble techniques. The info we used in our work is collected from the World Wide Web and contains news articles from various domains to cover most of the news rather than specifically classifying political news. The first aim of the research is to identify patterns in text that differentiate fake articles from true news. Within the proposed system we will extract different textual features from the articles using a machine learning tool and used the feature set as an input to the models. the training models were trained and parameter-tuned to obtain optimal accuracy. Some models have achieved relatively higher correctness than others. we'll use multiple performance metrics to compare the results for each algorithm. The ensemble learners have shown an whole better score on all presentation metrics as related to the separable beginners.

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Fake News Detection Using XLNet Fine-Tuning Model

Cited by 12 publications

References 17 publications

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

Induction of knowledge, attitude and practice of people toward a pandemic from Twitter: a comprehensive model based on opinion mining

English Text Recognition Deep Learning Framework to Automatically Identify Fake News

Research on the Ability to Detect Fake News with Machine Learning

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