Fake Review Detection

Abstract

The Fake Review Detection System: A Review SHIKHAR RAJ GUPTA, RESHU SINGH Computer Science and Engineering, Babu Banarasi Das Northern Indian Institute of Technology Lucknow, India ABSTRACT Fake Reviews:In the age of digital commerce, user-generated reviews significantly influence consumer decisions and business reputations. However, the growing prevalence of deceptive or fake reviews undermines the credibility of online platforms. This project proposes a Fake Review Detection System that leverages natural language processing (NLP) and machine learning techniques to identify and filter out fraudulent reviews. The system analyses textual patterns, reviewer behaviour, and metadata to distinguish between genuine and suspicious content. By training classification models on labelled datasets, it can detect subtle linguistic cues and behavioural anomalies associated with fake reviews. This solution aims to enhance user trust and support fair business practices by maintaining the integrity of online feedback. We analyse the linguistic, behavioural, and contextual features that differentiate fake reviews from genuine ones, emphasizing patterns such as excessive sentimentality, repetition, temporal anomalies, and user activity irregularities. A dataset of labelled reviews from diverse platforms is utilized to train and test our models.

Introduction

1. Importance of Online Reviews
Online reviews play a crucial role in consumer decision-making across industries like e-commerce, travel, hospitality, food delivery, healthcare, and apps. However, the rise of fake reviews—deceptive content posted to unfairly promote or discredit products or services—has threatened the reliability of such platforms, leading to misinformation and erosion of consumer trust.

2. Problem Statement

Consumers heavily rely on online reviews, but the widespread posting of fake reviews (positive or negative) by businesses or competitors harms both users and fair businesses. Traditional detection methods (like keyword filtering or star rating analysis) are inadequate, necessitating intelligent, scalable, and adaptable systems that can handle sophisticated fake content and evolving manipulation techniques.

3. Challenges

• Evolving Tactics: Fake reviewers adopt new methods to evade detection.

• High Volume: Platforms receive thousands of reviews daily, making manual filtering impractical.

4. Need for Effective Solutions

An effective system should:

• Use NLP, ML, and deep learning.

• Analyze reviewer behavior (e.g., posting patterns, sentiment).

• Apply graph-based models to find coordinated fake review networks.

• Be ethical, privacy-aware, and able to adapt to new threats.

5. Literature Review

a. Traditional Methods

• Rule-based systems focused on patterns like exaggerated sentiment or posting time.

• Often fail against sophisticated or bot-generated reviews.

b. Text and Linguistic Analysis

• Use of unigrams, sentiment analysis, POS tags.

• NLP helps uncover deeper semantics.

• Limitations: can’t detect human-like fake reviews.

c. Behavioral Analysis

• Study of reviewer activity, e.g., rating patterns, burst posting.

• Effective but limited by data accessibility.

d. Machine Learning

• Supervised algorithms (SVM, Random Forest, Logistic Regression) perform well but need large, high-quality labeled datasets.

e. Deep Learning

• Advanced models like RNNs, LSTMs, CNNs, and transformers (BERT) show strong results but require extensive data and computation.

6. Methodology

The system includes:

• Data Collection: From platforms like Amazon, Yelp, TripAdvisor.

• Preprocessing: Removing noise, tokenizing, normalizing.

• Feature Extraction:

  • Textual: TF-IDF, sentiment, review length.

  • Behavioral: Review timing, frequency, rating patterns, profile history.

• Model Training: Using classifiers like SVM, Random Forest, Gradient Boosting.

• Evaluation: Accuracy, precision, recall, F1-score, confusion matrix.

• Deployment (optional): Real-time integration with review systems.

7. Results and Evaluation Metrics

• Accuracy: Overall correctness.

• Precision: Correctly identified fake reviews out of all flagged.

• Recall: Percentage of fake reviews successfully detected.

• F1-Score: Balance between precision and recall.

• Confusion Matrix: Visual breakdown of true/false positives/negatives.

• AUC-ROC: Probability of distinguishing fake vs. real reviews.

• Execution Time and Scalability: Important for real-time applications.

• False Positive/Negative Rates: Indicators of trustworthiness.

• Human Review and Explainability: Ensure model transparency and trust.

8. Applications

• E-Commerce: Prevents rating manipulation, ensures fair competition.

• Travel & Hospitality: Guides trustworthy booking decisions.

• Food Delivery & Restaurants: Maintains review credibility on platforms like Yelp or Zomato.

• Social Media: Detects coordinated review spam or bot-driven engagement.

• App Stores: Prevents fake ratings that affect downloads.

• Job Portals: Ensures fair employer representation on sites like Glassdoor.

9. Future Directions

• Advanced AI/ML: Greater use of deep learning and NLP (e.g., BERT, GPT).

• Anomaly Detection: Identify unusual review behavior.

• Real-time Detection: Instant filtering of suspicious content.

• Cross-platform Integration: Holistic monitoring across multiple review sources.

• Privacy-preserving Analytics: Ensure ethical data use.

• Explainable AI: Enhance transparency and user trust.

Conclusion

Fake review detection systems are essential tools in today’s digital world, where online reviews strongly influence consumer choices and business reputations. These systems help identify and remove misleading or deceptive reviews, ensuring that only genuine feedback is shared on platforms like e-commerce websites, travel portals, and service-based applications. By using technologies such as machine learning, natural language processing, and behavioural analysis, these systems can analyze patterns in text, reviewer behaviour, and submission timing to detect reviews that are likely to be fake. This not only helps customers make more informed decisions but also promotes fair competition among businesses. As fake review strategies become more advanced, detection systems must also evolve to stay effective. Future developments may include real-time analysis, improved accuracy through AI, and stronger safeguards for user privacy and fairness. In summary, fake review detection systems are vital for building trust online. They help create a transparent digital environment where users can rely on honest opinions, and businesses can compete fairly based on the quality of their products or services.

References

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Copyright

Copyright © 2025 Shikhar Raj Gupta, Reshu Singh, Asst. Prof. Mr. Satendra Kumar Vishwakarma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.