Smart Edge-based Fake News Detection using Pre-trained BERT Model

Guo, Yuhang; Lamaazi, Hanane; Mizouni, Rabeb

doi:10.1109/wimob55322.2022.9941689

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…On the other hand, the current suggested solutions for identifying fake news on centralized platforms primarily focus on analyzing the news content and not considering the location of the news. Reference 16 proposed a region-based distributed approach for detecting fake news, which operates in a mobile crowdsensing environment. The framework chose workers based on their accessibility in a particular area, who oversee gathering and sharing the news with the closest edge node.…”

Section: Literature Surveymentioning

confidence: 99%

ANN: adversarial news net for robust fake news classification

Maham,

Tariq,

Khan

et al. 2024

Sci Rep

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With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

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Section: Literature Surveymentioning

confidence: 99%

ANN: adversarial news net for robust fake news classification

Maham,

Tariq,

Khan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Text classification is an important task in natural language processing. It is the foundation of many tasks [1,2] and has a wide range of applications in fields such as sentiment analysis [3], spam detection [4], and fake news detection [5]. Due to its expansive applicability, text classification demands substantial consideration and emphasis.…”

Section: Introductionmentioning

confidence: 99%

ConKgPrompt: Contrastive Sample Method Based on Knowledge-Guided Prompt Learning for Text Classification

Wang,

Zeng,

et al. 2023

Electronics

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Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning methods can bring state of the art (SOTA) performance to pre-trained language models (PLMs) in text classification and transform a classification problem into a masked language modeling problem. The crucial step of prompt learning is to construct a map between original labels and the label extension words. However, most mapping construction methods consider only labels themselves; relying solely on a label is not sufficient to achieve accurate prediction of mask tokens, especially in classification tasks where semantic features and label words are highly interrelated. Therefore, the accurate prediction of mask tokens requires one to consider additional factors beyond just label words. To this end, we propose a contrastive sample method based on knowledge-guided prompt learning framework (ConKgPrompt) for text classification. Specifically, this framework utilizes external knowledge bases (KBs) to expand the label vocabulary of verbalizers at multiple granularities. In the contrastive sample module, we incorporate supervised contrastive learning to make representations more expressive. Our approach was validated on four benchmark datasets, and extensive experimental results and analysis demonstrated the effectiveness of each module of the ConKgPrompt method.

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“…Using parallel computation, MEC is considered an optimal solution for real-time applications while providing high optimization of communication performances and services [4] [5]. An Edge node can be a decision-maker that applies some assessment, detection, or prediction strategies to the sensed data from the workers [6]. The Edge node, deployed as a local or global entity, can play an important role in propagating information between different entities at the same level, not only between the user and the server as in traditional work [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

et al. 2022

Self Cite

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Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task. A sensing task can be one-time or continuous, with multiple readings collected over time. Integrating MEC and continuous sensing in MCS is challenging due to many factors, including workers' mobility, edge node placement, task location, Reputation, and data quality. In addition, guarantying cooperative communication in the presence of Anomalous data while maintaining a high quality of service (QoS) is a fundamental issue in continuous sensing. A stability-based edge node selection and anomaly detectionbased decision-making framework for worker recruitment in continuous sensing is proposed to address these challenges. It can a) Select the most stable edge nodes in the area of interest (AoI), b) Dynamically cluster the workers according to their movement in the AoI, c) Locally detect and eliminate anomalies within the sensing data, and d) Adopt a feedback mechanism that ensures the cooperation between the edge nodes to eliminate untrustworthy workers in the whole sensing period and future tasks. A real-life dataset is used to evaluate the efficiency of the proposed framework. Results show that the framework outperforms the baselines by achieving higher QoS while introducing lower delay, energy consumption, and less resource consumption.

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Smart Edge-based Fake News Detection using Pre-trained BERT Model

Cited by 8 publications

References 10 publications

ANN: adversarial news net for robust fake news classification

ANN: adversarial news net for robust fake news classification

ConKgPrompt: Contrastive Sample Method Based on Knowledge-Guided Prompt Learning for Text Classification

Trust-3DM: Trustworthiness-Based Data-Driven Decision-Making Framework Using Smart Edge Computing for Continuous Sensing

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