You’re about to book a hotel, buy a new gadget, or try a restaurant. What’s the first thing you do? If you’re like most people, you dive headfirst into the online reviews, trusting the digital word-of-mouth of strangers. But in an era where five-star ratings can be bought and sold, how can we separate genuine experiences from carefully crafted fiction? The answer, surprisingly, lies in the language itself. Welcome to the world of forensic linguistics, where sentence structure and word choice can unmask a lie.

Deceptive writing leaves behind linguistic fingerprints. While professional liars might be good at crafting a believable story, they subconsciously fall into predictable language patterns. Researchers at institutions like Cornell University have spent years analyzing millions of reviews, and along with cutting-edge AI, they’ve identified several red flags. By learning to spot these tells, you can become a more savvy online sleuth.

Setting the Scene vs. Describing the Object

One of the most significant differences between real and fake reviews is the use of concrete detail. A genuine reviewer, recounting a real experience, tends to focus on specific, tangible things. Their brain is retrieving memories of objects, spaces, and sensory information.

A deceptive writer, on the other hand, doesn’t have these memories to draw from. Inventing specific, consistent details is cognitively demanding—it’s hard work! It’s much easier to create a generic narrative. They often “set the scene” with broad, story-like language instead of describing the actual product or service.

Consider these two reviews for a hotel:

• Potentially Fake: “My family and I wanted a perfect getaway for my husband’s birthday, and this hotel delivered! We had the most amazing vacation. Everything was simply perfect, and we made memories that will last a lifetime. Highly recommended for any special occasion!”
• Likely Genuine: “The check-in was quick, but the room on the third floor had a faint smell of chlorine. The king-sized bed was comfortable, and I appreciated the blackout curtains. The best part was the rooftop pool, though it got crowded after 3 PM and we had to wait for a lounge chair.”

The first review is all narrative (“getaway”, “special occasion”, “memories”). It uses vague, emotional words (“perfect”, “amazing”) but tells you nothing concrete about the hotel itself. The second review is packed with specifics: third floor, chlorine smell, king-sized bed, blackout curtains, crowded pool. It presents a balanced view rooted in a real-world experience, warts and all.

Pronoun Overload: The Tell-Tale “I”

Here’s a counterintuitive one: fake reviewers often use first-person pronouns like “I” and “me” far more frequently than genuine reviewers. You would think someone telling a personal story would naturally say “I”, but deceivers tend to overdo it.

Why? It’s a subconscious act of overcompensation. The writer is trying to establish their credibility and insert themselves into the narrative to make it feel more personal and believable. “I am a real person, and I’m telling you my honest opinion!” This psychological need to assert their presence often results in a text peppered with “I believe”, “I think”, and “In my opinion.”

A genuine reviewer is more likely to focus on the object of the review. Their writing is more descriptive of the product or experience itself.

• Potentially Fake: “I was so excited to try this new blender. I have to say, I am completely blown away by its performance. I made a smoothie this morning, and I’ve never had one that was so smooth.”
• Likely Genuine: “This blender is powerful. It pulverized frozen strawberries and spinach in about 30 seconds. The base is heavy, so it doesn’t vibrate across the counter, but the pitcher is plastic, not glass, which was a surprise.”

Notice how the second example focuses on the blender’s attributes—its power, its stability, its materials. The first is a story about the reviewer’s feelings.

The Language of Extremes and Action-less Verbs

Emotional language is a powerful tool, and fakers wield it with abandon. Fake positive reviews are often loaded with superlatives and adverbs, creating a sense of breathless excitement. Think “absolutely amazing”, “incredibly perfect”, “the best ever”, and “life-changing.”

Conversely, fake negative reviews often create high drama. They use words like “disaster”, “horrible”, “nightmare”, and “scam” to frame their experience as a cautionary tale.

Another subtle clue is the choice of verbs. Genuine reviews are often full of action verbs that describe what someone did or what the product did. Fake reviews lean more heavily on adjectives and adverbs to create a feeling without describing an action.

Let’s look at two negative reviews for a piece of self-assembly furniture:

• Potentially Fake: “This was a complete and utter disaster. I have never been so frustrated in my entire life. The whole experience was a horrible nightmare. Do not buy this garbage product!”
• Likely Genuine: “The instructions showed three types of screws, but the package only contained two. I couldn’t attach the legs properly. I called customer service and was on hold for 45 minutes before the line disconnected. The shelf is now sitting half-finished in my garage.”

The first review is pure emotion with no specifics. The second is a play-by-play of the problem, using action verbs: “showed”, “contained”, “couldn’t attach”, “called”, “was on hold”, “disconnected”, “is sitting.” This is the language of a real, frustrating experience.

Enter the Algorithms: Linguistics at Scale

While you can train your own brain to spot these patterns, the real battle against fake reviews is being fought by machines. Companies like Amazon and Yelp employ teams of engineers and data scientists who use Natural Language Processing (NLP)—a branch of AI—to analyze content at a massive scale.

These algorithms are trained on vast datasets of known genuine and fake reviews. They learn to spot the linguistic tells we’ve discussed, but with far greater precision and speed. They can detect:

• N-gram frequencies: Common two or three-word phrases that appear more often in fakes (e.g., “my husband and I”).
• Part-of-speech analysis: The ratio of nouns and verbs to adjectives and adverbs. Fakes often have more adjectives; genuines have more concrete nouns.
• Textual similarity: Identifying multiple reviews across different products that are slightly re-worded versions of each other.

This has become a high-tech arms race. As the detection algorithms get smarter, so do the fraudsters, who now sometimes use AI to generate more “human-sounding” fakes. It’s a constant cat-and-mouse game played out in the star ratings of the internet.

So, the next time you find yourself wading through a sea of reviews, put on your linguistic detective hat. Pay less attention to the star rating and more to the words. Look for concrete details, notice the balance of pronouns, and be wary of extreme emotional language. While no single clue is definitive proof, a combination of these red flags can help you cut through the digital fog and find an opinion you can actually trust.