EdgeFNF: Toward Real-time Fake News Detection on Mobile Edge Computing | Semantic Scholar (2024)

The proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings, contributing to improved sentiment analysis and subsequently lead to more accurate fake news detection.

A region-based distributed fake news detection framework is proposed that is deployed in a mobile crowdsensing (MCS) environment where a set of workers responsible for collecting news are selected based on their availability in a specific region.

A fake news detection mobile app with a device-based prediction model based on the small language model ALBERT that can achieve real-time, accurate detection of fake news is designed and developed.

A novel model for early detection of fake news on social media through classifying news propagation paths that incorporates both recurrent and convolutional networks which capture the global and local variations of user characteristics along the propagation path respectively, to detect fake news.

This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models and the proposed IFND dataset achieved satisfactory results.

A network computing framework Quick Fake News Detection (QcFND) is proposed, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks to infer whether to trust the received traffic reports.

This study addresses the previous concern, offering a comprehensive review of the architectural elements of BD batch query deployment models and environments, and provides an extensive survey of the modern BD deployment architectures, categorizing them based on their underlying infrastructure.

The findings from the experiments conducted have shown that real-time adversarial identification and profiling during the UFP could significantly increase the accuracy during the learning process with a high degree of certainty and paves the way towards an automated adversarial detection and profiling approaches on the Internet of Cognitive Things (ICoT).

A step towards a Learning-enabled (LE) Virtual Sensor Forensic (VSF) architecture that is compliant with edge intelligence technology, which is based on an initially proposed generic VSF architecture.

A novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud) that relies on a systematic security analysis methodology and a completely automated security assessment framework that enables the mapping of BigCloud security domain knowledge to the best practices in the design phase.

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