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Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model.

Jitendra Kumar Samriya¹, Amit Kumar², Ashok Bhansali³

¹CSE, IIIT Sonepat, Sonepat, India.

Scientific Reports

|April 19, 2025

Summary

This study introduces a privacy-focused system using blockchain and sentiment analysis to detect fake online recommendations. The novel approach effectively identifies and analyzes deceptive content, safeguarding user trust and combating misinformation.

Keywords:

Blockchain model Fake web recommendations Privacy analysis Sentimental analysis federated learning

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Area of Science:

Computer Science
Information Security
Artificial Intelligence

Background:

The proliferation of fake news and deceptive online content poses significant risks to individuals and society.
Manual verification of online information is impractical due to the sheer volume and sophisticated nature of fabricated content.
Advanced neural language models (NLMs) can generate realistic fake reviews, impacting consumer choices and online platforms.

Purpose of the Study:

To develop a privacy-focused system for detecting and analyzing fake web recommendations.
To leverage blockchain technology and sentiment analysis for enhanced fake news detection.
To improve the reliability of online reviews and protect consumers from manipulation.

Main Methods:

Utilized sentiment-based features extracted from web recommendations as input data.
Employed a generative convolutional Bernoulli Bayes neural network for feature extraction and classification.
Integrated blockchain technology with federated learning to ensure network privacy.

Main Results:

The proposed system achieved high performance metrics: 99% accuracy, 94% precision, 93% area under the curve, 94% recall, and 96% F-measure.
Demonstrated the ability to distinguish between spam and non-spam content using tweet data and sentiment analysis.
Validated the effectiveness of the developed predictive model in identifying fake recommendations.

Conclusions:

The integration of blockchain and sentiment analysis offers a robust solution for detecting fake web recommendations.
The developed privacy-focused system effectively mitigates risks associated with online misinformation.
This research provides a valuable framework for enhancing trust and security in online information ecosystems.