Efficient Fake News Detection Mechanism Using Enhanced Deep Learning Model

Ahmad, Tahir; Faisal, Muhammad Shahzad; Rizwan, Atif; Alkanhel, Reem; Muthanna, Ammar

doi:10.3390/app12031743

Cited by 23 publications

(12 citation statements)

References 21 publications

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“…It is not hard to see how predictive analytics can be used in the healthcare industry to help healthcare providers understand the complexities of clinical cost, find the most effective treatment options, and anticipate future healthcare trend lines relying on the habits, lifestyles, and diseases of their patients [ 23 ]. Natural language processing (NLP) and Data Mining are mostly applied in predictive-analytics-based approaches [ 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

et al. 2022

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Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.

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Section: Related Workmentioning

confidence: 99%

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

et al. 2022

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“…Song et al proposed a rumor detection model based on time propagation that integrates structure, content semantics and time information, considering the constant dynamic updating of nodes and edges in the real network, and modeled the time evolution mode of real-world news as a graph evolving under the setting of continuous-time dynamic diffusion network [10] . Ahmad et al proposed a enhanced deep learning model BiLSTM-RNN based on social and content to detect sudden rumors on social media networks, which improved the detection performance [11] .…”

Section: Based On the Identification And Detection Of Rumormentioning

confidence: 99%

A multi-blockchain system application based on improved PBFT consensus mechanism for online rumor co-governance

Chen

Liu

2022

Preprint

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At present, there are difficulties in the governance of Internet rumors, such as slow control and information barriers. In the face of the confused and complex social public opinion environment, it is urgent to innovate technical means to break through the bottleneck of rumor prevention and control. Firstly, based on the architecture characteristics and core technologies of blockchain, this paper designs a multi-blockchain system architecture for the collaborative co-governance scenario of online rumors, and puts forward the design ideas of smart contracts and consensus mechanisms for the functions of different chains, so as to enable different chains and different nodes within each chain to achieve collaborative interaction of rumor dispelling and rumor suppression strategies. Secondly, according to the requirements of the rumor co-governance scenario, we improve the original PBFT consensus algorithm on the basis of analyzing the defects, and propose the RK-PBFT consensus mechanism based on rumor refutation reliability and K-Medoids clustering algorithm. Through the simulation experiment, the algorithm is compared before and after the improvement. The results show that the delay of the proposed improved consensus algorithm is significantly reduced and the throughput is greatly improved, in addition, in the case of the same number of nodes, the more the number of packets, the lower the delay and the higher the throughput. This means that the proposed consensus algorithm not only improves the delay and throughput performance of the original algorithm, but also ensures the reliability and operation efficiency of the rumor co-governance system.

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“…Unfortunately, the presence of fake sources, noise, and misinformation has an influence on the quality of tweet content. We concentrate on Twitter conversations in order to improve the quality of tweets Ahmad ( 2022 ). Several authors have proposed various methods for calculating the credibility of Twitter content.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors have proposed various methods for calculating the credibility of Twitter content. Some researchers employ machine learning techniques, while others employ graph-based techniques and human perception judgments Ahmad ( 2022 ) Al-Khalifa and Al-Eidan ( 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based credibility conversation detection approaches from social network

Fadhli

Hlaoua

Omri

2023

Soc. Netw. Anal. Min.

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In recent years, the social networks that have become most exploited sources of information, such as Facebook, Instagram, LinkedIn, and Twitter, have been considered the main sources of non-credible information. False information on these social networks has a negative impact on the credibility of conversations. In this article, we propose a new deep learning-based credibility conversation detection approach in social network environments, called CreCDA. CreCDA is based on: (i) the combination of post and user features in order to detect credible and non-credible conversations; (ii) the integration of multi-dense layers to represent features more deeply and to improve the results; (iii) sentiment calculation based on the aggregation of tweets. In order to study the performance of our approach, we have used the standard PHEME dataset. We compared our approach with the main approaches we have studied in the literature. The results of this evaluation show the effectiveness of sentiment analysis and the combination of text and user levels to analyze conversation credibility. We recorded the mean precision of credible and non-credible conversations at 79%, the mean recall at 79%, the mean F1-score at 79%, the mean accuracy at 81%, and the mean G-Mean at 79%.

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Efficient Fake News Detection Mechanism Using Enhanced Deep Learning Model

Cited by 23 publications

References 21 publications

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

A multi-blockchain system application based on improved PBFT consensus mechanism for online rumor co-governance

Deep learning-based credibility conversation detection approaches from social network

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