Fake Review Detection in 2024: Overview, Methods & Case Studies

Online reviews are a crucial factor influencing consumers‘ purchase decisions today. According to surveys, around 80% of customers read reviews before making a purchase.

However, the rise of fake and fraudulent reviews threatens to undermine consumers‘ trust and mislead purchasing choices. In 2021 alone, over 2.7 million fake reviews were detected globally, accounting for approximately 50% of all 5-star ratings.[^1] [^1]: Statista. (2021). Distribution of online fake reviews that were removed in 2021, by star rating. https://www.statista.com/statistics/1310797/global-fake-reviews-removed-by-star-rating/

Clearly, fake reviews remain a widespread problem that can deceptively boost or unfairly damage brands. This comprehensive guide will examine:

  • How various types of fake reviews are generated
  • Methods for detecting and analyzing fake reviews
  • Real-world examples and case studies

As an expert in data extraction with over a decade of web scraping experience, I‘ve witnessed firsthand the rise of fake reviews and their impact across the industry. Here I‘ll share my insights into the emerging techniques for combating fake reviews in 2024 and beyond.

Categories of Fake Reviews

Fake reviews typically fall into three main categories:

1. Human-Written Fake Reviews

The most basic type of fake review is manually written by a human. Some brands pay freelance writers or use review services to generate fake positive reviews for their own products. Competitors may also hire people to write negative fake reviews about rival brands.

These reviews are written by actual people, though sometimes based on pre-provided templates or formulas. The goal is to artificially boost ratings or damage the reputation of specific targets.

According to a 2021 survey by BrightLocal, over 33% of consumers have seen a business reply claiming fake reviews were posted by a competitor, indicating this remains a common occurrence.[^2]

[^2]: BrightLocal. (2021). Local Consumer Review Survey 2021. https://brightlocal.com/research/local-consumer-review-survey-2021/

2. AI-Generated Fake Reviews

Advances in AI text generation technology have enabled the automated mass-production of fake reviews. Modern language models can analyze patterns from real review data and generate synthetic reviews that sound authentic.

This allows creators to quickly generate thousands of fake but believable reviews customized to specific products, brands, or services. According to estimates up to 35-45% of online reviews may already be AI-generated fakes.

3. Incentivized Fake Reviews

Many fake reviews are created by offering customers incentives such as discounts or free products in exchange for reviews. This yields biased reviews that only provide positive feedback, rather than honest opinions.

A 2022 BrightLocal survey found 60% of consumers reported being offered incentives specifically in exchange for a 5-star review.[^3] [^3]: BrightLocal. (2022). Local Consumer Review Survey 2022. https://brightlocal.com/research/local-consumer-review-survey-2022/

While influenced by incentives, these incentivized reviews are still written manually by actual consumers, making their detection more challenging.

4. Paid Fake Reviews

Fake review services have emerged allowing businesses to outright pay for fake reviews. For example, Facebook groups connect sellers with buyers of reviews, paying $5-$15 per Amazon review.

A 2022 analysis identified over 10,000 Facebook groups created specifically for buying and selling fake reviews, some with over 40,000 members.[^4] [^4]: Amazon. (2022). Amazon targets fake review fraudsters on social media. https://www.aboutamazon.com/news/policy-news-views/amazon-targets-fake-review-fraudsters-on-social-media

While clearly fraudulent, fake review services remain prevalent across social media and the dark web.

Fake Review Category Description
Human-written Manually created reviews, often using templates
AI-generated Automated reviews from language models
Incentivized Biased positive reviews in exchange for rewards
Paid Services selling fake reviews for money

Understanding the different categories of fake reviews allows us to better analyze detection approaches and patterns. Next, let‘s explore popular techniques for identifying fake reviews.

Fake Review Detection Methods

Various manual and automated techniques are leveraged to detect and combat fake reviews, including:

Manual Review

Manual analysis involves human reviewers reading and scrutinizing reviews to determine if they appear suspicious, unnatural, or fake. Telltale signs may include:

  • Overly generic praise or scathing criticism
  • Mismatched ratings and review text
  • focuses on irrelevant details

However, manual review is slow, expensive, and inaccurate at scale. One study found human reviewers only identify fake reviews with 57% accuracy on average.[^5] [^5]: Plotkina, D., Munzel, A., & Pallud, J. (2020). Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews. Journal of Business Research, 109, 511-523. https://doi.org/10.1016/j.jbusres.2018.07.043

Sentiment Analysis

Sentiment analysis uses AI to automatically scan reviews and classify their sentiment as positive, negative, or neutral.

Fake reviews often demonstrate more extreme sentiment compared to authentic reviews. Outlier detection identifies reviews with:

  • Excessive 5-star praise
  • Scathing 1-star criticism
  • Mismatched text and ratings

72% of removals for fraudulent Amazon reviews were 5-star ratings, suggesting strong skewing.[^1]

Text Analysis

AI text analysis tools can check for signs of automated or templated fake reviews:

  • Repeated phrases and patterns
  • Unnatural transitions
  • Poor grammar and spelling

Research finds AI-generated reviews contain more punctuation and stop words like "the", "and", etc.[^6] [^6]: Ren, Y., Zhang, Y., Zhang, Z., Ji, D., He, S., & Liu, T. (2019). Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency. arXiv preprint arXiv:1909.06723.

Reviewer Profiling

Analyzing patterns of reviewers themselves can uncover suspicious behaviors:

  • Users submitting an abnormal volume of reviews
  • Review spikes over short periods
  • Details missing from profiles

89% of reviewers removed from Yelp for fraud had reviewed only one business.[^7] [^7]: Luca, M., & Zervas, G. (2016). Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud. Management Science, 62(12), 3412-3427. http://dx.doi.org/10.1287/mnsc.2015.2304

Network Analysis

Looking at connections between groups of reviewers and businesses can uncover linked fake review networks and orchestrated campaigns.

For example, a sudden influx of new accounts all reviewing the same product indicates coordinated fakes. Facebook groups explicitly create these networks.[^4]

Rating & Timing Analysis

Statistically analyzing patterns in ratings, timing, and other metadata can identify questionable anomalies:

  • Excessive 5-star ratings
  • Review spikes on certain days
  • Mismatched peak hours

Integrating these signals using AI helps surface the most suspect reviews for evaluation.

Real-World Examples

Next let‘s examine some real-world examples of fake review detection and prevention in action:

Yelp Review Filtering

Yelp uses both automated filtering and manual moderation to detect approximately 25% of reviews as suspicious. These flagged reviews are excluded from businesses‘ overall star ratings.[^8] [^8]: Anderson, M., & Magruder, J. (2012). Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database. The Economic Journal, 122(563), 957-989. https://doi.org/10.1111/j.1468-0297.2012.02512.x

Yelp also issues consumer alert warnings on business pages when their models detect attempts to astroturf fake positive reviews.

Amazon Sues Fake Review Brokers

In 2022, Amazon identified and filed lawsuits against operators of over 10,000 Facebook groups created explicitly to buy and sell fake Amazon reviews. Some groups had over 43,000 members dedicated to review fraud.[^9]

[^9]: Amazon. (2022). Amazon targets fake review fraudsters on social media. https://www.aboutamazon.com/news/policy-news-views/amazon-targets-fake-review-fraudsters-on-social-media

The legal action aimed to shut down these review broker networks and hold their organizers accountable. This demonstrates brands taking direct action against sources of fake reviews.

App Store Review Analysis

A comprehensive 2019 study analyzed over 22 million App Store reviews across approximately 1.4 million apps to detect fraudulent ratings and reviews.[^10]

[^10]: Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering, 24(6), 3316-3355. https://doi.org/10.1007/s10664-019-09706-9

Methodology of the App Store review analysis study

Using machine learning classifiers, the researchers flagged approximately 35% of App Store reviews as likely fake or fraudulent.

Over 60,000 apps exhibited strong indicators of fake review activities. This demonstrates the wide reach of fake reviews in inflating app ratings.

Hotel Review Analysis

FakeSpot, a leading fake review detection site, analyzed over 127 million TripAdvisor hotel reviews using AI and neural networks.

They concluded between 10-14% of TripAdvisor hotel reviews are likely fake, with over 1.2 million total fraudulent reviews detected.[^11]

[^11]: Evans, B. (2021). Tripadvisor Reviews: Real or Fake? FakeSpot. https://www.fakespot.com/tripadvisor-reviews-real-or-fake

Reviews were identified based on unnatural language patterns, minimal details, and suspicious behaviors. This showcases deep learning AI applied across massive review datasets.

Emerging Innovations

As AI text generation advances, fake reviews are becoming more sophisticated. Here are several promising emerging techniques:

  • Multimodal deep learning – Combining text, metadata, human behavior patterns, and more for integrated fake review detection.
  • Graph learning – Uncovering connections between fake reviewers and targets to trace broader networks.
  • Neural language models – Detecting the subtle "style" of individual real reviewers vs. AI-generated text.
  • Blockchain-verified reviews – Cryptographic proof on ledgers to authenticate review origins and content.

Ongoing innovations in AI, deep learning, and blockchain point the way to more automated and foolproof fake review prevention. Review platforms are actively investing in these technologies – aiming to sustain consumer trust through review integrity.


This guide provided an extensive look at the growing issue of fake reviews across categories like human-written, AI-generated, incentivized, and paid. We explored current methods for identifying fake reviews as well as real-world examples of platforms including Yelp and Amazon combat fraud.

Fake reviews erode consumer trust and allow brands to benefit through deception. With AI advances, fake reviews are becoming more prevalent and advanced.

Maintaining reliable online reviews requires a combination of technology innovation, vigilant monitoring, and consumer awareness. As an expert in data extraction and analysis, I see robust fake review prevention as crucial to sustaining trust in the digital economy.