'The Yahoo Boys' Shows Nigeria’s Scammers Are Still People ⁠20%

By Sarah Weinman⁠22%

7/10/2026, 6:15:13 PM

BS Summary: This article contains 36 faulty reasoning types, including Negativity Bias, Indoctrination, and Pessimism Bias, with Biased Writer Voice as the most egregious example at 31.1% saturation with 763 hits. Analysis detected 4,144 faulty-reasoning hits from 2,452 analyzed words, generating a BS Score of 34.4% and a BS Rank of ⁠20% (17,047 of 21,172 articles). This article is better (less manipulative) than 80.50% of the article peer group.

‘The Yahoo Boys’ shows how con artists can be victims too. 
July 10, 2026, 2:15 PM 
By Sarah Weinman , an author and the crime and mystery columnist for the New York Times Book Review. 
A man in a dark shirt and grey pants walks away from a small storefront with a corrugated metal roof, looking down at a piece of paper in his hands. 
To the left, two other men stand near a pair of small, portable gas generators on the ground. 
Banners on the shop's front advertise services such as passport, photocopy, and printing. 
Men outside a cybercafe in Lagos on April 7. 
Temiloluwa Johnson/Getty Images 
Science and Technology 
Carlos Barrágan just wanted to understand what had happened to his mother. 
How could she, an accomplished, professional, middle-class woman who had raised three sons on her own, fall prey to a romance scammer pretending to be a U.S. soldier in Syria who was likely in reality a young man based in Lagos, Nigeria? 
As first detailed in his 2023 feature for the Atavist magazine and now expanded into his first book, The Yahoo Boys: Love, Deception, and the Real Lives of Nigeria’s Romance Scammers , Barrágan, a journalist in Madrid, thought he was going to find some measure of justice for his mother, and also take a closer look at the male loneliness epidemic. 
What he discovered, through this outstanding work of immersion journalism (written in English, his second language), was a far more complex story at the intersection of a persistent economic crisis; global hungers literal and metaphorical; and the brutal need to survive at all costs. 
In the United States alone, romance scams target nearly one in 10 adults aged 50 and over, leading to more than $1.1 billion in total losses as of 2023—“the highest reported losses for any form of imposter scam,” according to the Federal Trade Commission. 
The book cover for "The Yahoo Boys" by Carlos Barragán features a yellow background with a large black silhouette of a man looking downward. 
Inside the lower portion of his silhouette, a smaller, yellow silhouette depicts a woman with her hair in a bun looking at a smartphone. 
The main title "THE YAHOO BOYS" is written in a large, blocky black font at the top, with the subtitle "LOVE, DECEPTION, AND THE REAL LIVES OF NIGERIA'S ROMANCE SCAMMERS" positioned to the left of the man's silhouette. 
The Yahoo Boys: Love, Deception, and the Real Lives of Nigeria’s Romance Scammers , Carlos Barrágan, Farrar, Straus and Giroux, 304 pp., $29, June 2026 
Barrágan makes what could be considered a controversial choice: to spend his time with four romance scammers, the titular Yahoo Boys (named for the email service that scammers largely used in the 2000s) living and surviving in Ikotun. 
Ikotun is part of Lagos’s mainland, far poorer than the island, where the obscenely rich live alongside expats. 
Doing so centers perpetrators over victims, but Barrágan’s reasoning turns out to be sound, because the motivations and desires of the scammers hinge on a desperation similar to that of their victims. 
The outcomes may be different, but the loneliness and need are the same. 
Scam victims see outreach on social media, dating apps, or text messages as a genuine overture, one that makes them feel seen in a world where true emotional bonds are increasingly replaced with ersatz online strands. 
That isolation and lack of community makes it easy for them to fall for online illusions, even implausible ones. 
Even a line as banal as, “How was your day, babe?” 
goes a long way: “If you give them attention, you have their heart. 
If you have their heart, you have their money,” one scammer tells Barrágan. 
Gender also wasn’t a big deal: Many of the scammers Barrágan talked to were as likely to scam men as they did women. 
A hand holds a smartphone displaying a text message conversation with a contact named "Ancel." 
The chat includes a sent photograph of a smiling woman posing next to a bouquet of red roses, accompanied by an affectionate paragraph of text. 
Below it, subsequent chat bubbles show a brief exchange of text messages and a message containing three kiss emojis. 
A message and photo that Shreya Datta, a tech professional who was a victim of an online scam known as “pig butchering,” shared with a person who turned out to be a scammer is displayed on her phone in Philadelphia on Feb. 
9, 2024. 
Bastien Inzaurralde/AFP via Getty Images 
The scammers, in their teens and 20s, grew up in a country going through a continual cycle of boom and bust, fueled by oil money and hyperinflation. 
Nigerians have few options for a real future: Make the equivalent of pennies on the dollar doing honest work, or hundreds (or more) U.S. dollars in one fell swoop from working off well-worn scripts to charm lonely Westerners out of their hard-earned money. 
“Inflation is crazy. 
Prices are skyrocketing. 
The only thing in Nigeria young people can do to survive is joining Yahoo. 
No office work can give you the kind of money that Yahoo will give you,” one scammer told Barrágan. 
If that account sounds somewhat self-serving, you’d be right. 
Each of the romance scammers Barrágan profiles operates in a world of moral discoloration, a spectrum of darker gray where redemption is a faraway joke. 
Biggy, naming himself after the murdered rapper Biggie Smalls, seems the most self-aware, but he can’t quite reconcile his ability to feel empathy for his victims with the survival itch he must always scratch as the money he scams slips through his fingers yet again, largely spent on drugs, booze, and other markers of the high life. 
Chibuike, in dirty clothes and with drowned hopes, seems to spin unbelievable yarns, but Barrágan corroborates his distended horror story of scamming an Irish woman, “Theresa” (all the victims’ names are pseudonyms) for years by pretending to be the WWE wrestler Cody Rhodes, only to lose it all through drugs, profligate spending, and getting fleeced by his girlfriend. 
Cautionary tales abound, none more than Azeez, a teenage boy with two paths available to him—either the moral, churchgoing approach of his beloved grandmother or the instant cash flaunted by his Yahoo Boy pals—that fork into the same barren cul-de-sac. 
Finally, there is Richie, alternately feeling deep remorse and utter disdain towards Trisha, the U.S. woman he roped into laundering money for him from a variety of scams. 
He ruined her life, and now she’s dead (with pictorial proof to boot) but as Barrágan demonstrates in a bravura journey to Kentucky, Trisha’s home state , to figure out the truth, even the scammed have some desperate tricks up their sleeve. 
An over-the-shoulder view shows a person's hands counting a large stack of paper currency. 
In the foreground, another small bundle of cash, a set of keys, and bowls of food rest on a dark wooden table. 
In the background, out of focus, another person wearing a patterned shirt sits on a wooden bench with their hands on their hips. 
A man counts Nigerian naira as a customer exchanges money in Ikoyi island in Lagos on Sept. 
16, 2024. 
Olympia de Maismont/AFP via Getty Images 
Barrágan generally keeps the focus tight on his chosen four subjects, presenting them as fully fledged (if still extremely immature) human beings vacillating between different degrees of desperation, which is never fully slaked even after the influx of riches from a successful romance scam. 
But he also zooms out to the larger economic and historical picture, reminding readers that such scams have proliferated, albeit in different and less technologically adroit forms, since the beginning of the 20th century, as oppressed Nigerians figured out how to extract money from their colonial oppressors. 
He also points out the difference between Nigerian-based scams, which operate in a more decentralized manner (often through peer pressure) than the more organized networks operating out of Southeast Asia. 
Scamlands , Snigdha Poonam’s recently published (and excellent) investigation into the various scams that have become so pervasive in eastern India, Cambodia, and throughout the globe, points out that “fraud is no longer furtive…it has become the foundation of entire economies, breaking moral compasses but also blurring boundaries.” 
Scamming hasn’t just upended the social order, it has become the social order, reshaping power dynamics, warping expectations, and accelerating the boom-bust cycles that were already heading off a sharp cliff. 
An office room filled with people seated at desks with computer monitors, several of whom have hooded sweatshirts pulled over their heads or fabric covering their faces. 
In the foreground, a person with reddish hair wearing a grey hoodie sits with their head down and hands covering their face. 
Several men in uniforms stand in the background monitoring the room. 
Workers sit at their desks during a raid by agents of the Presidential Anti-Organised Crime Commission and the National Bureau of Investigation at an office of a suspected online scam farm in Manila, Philippines, on Jan. 
31, 2025. 
Jam Sta Rosa/AFP via Getty Images 
In a sign of how fast things move in the world of scams, The Yahoo Boys reads like a recent-history chronicle, because the romance scammers Barrágan chronicles in such painstaking detail are about to face a new existential crisis: generative AI. 
“Romantic AI chatbots run on the same emotional logic as Yahoo Boys,” Barrágan writes. 
“Both listen, flatter, and mirror desire, building the illusion of the ‘perfect other,’ while making the lonely feel seen. 
And in return, both ask for money (or at least training data). 
But a chatbot doesn’t sleep. 
It never forgets its backstory, never mixes up its lies, and can manage a hundred simultaneous love affairs without slipping. 
Unlike a Yahoo Boy, it doesn’t need to invent wild excuses about a missed flight to Atlanta. 
Because a chatbot doesn’t pretend to be real. 
It is just a fake relationship without the betrayal.” 
Fake relationships seem to be the grim future, with dating app CEOs like Whitney Wolfe Herd excitedly predicting that people using their products will use AI personas before human contact begins. 
Articles about people falling in love with chatbots and dying of preventable conditions ( including suicide)— or even committing murder —after extensive chatbot use, keep proliferating. 
What’s a romance scammer to do when they, too, might be training the tool that replaces them? 
The answer, at least for some scammers in Southeast Asia, is to go all in, using AI as their English interlocutor with would-be victims. 
The Yahoo Boys makes clear that there are no winners in this global sucker’s game. 
Those in Western countries who are scammed for years sometimes lose astronomical sums of money and end up with ruined lives. 
But the scammers’ lives are also ruined—by persistent societal corruption, inequality, malnutrition, and the lack of real opportunities to better themselves other than the quick lure of temporary enrichment often followed by a swifter descent into drugs, poverty, and additional instability. 
The reality is that the outcome was rigged long before the players were ever born, and will stay rigged long after they die. 
This post appeared in the FP Weekend newsletter, a weekly showcase of book reviews, deep dives, and features. 
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Sarah Weinman is an author, most recently of Without Consent (Ecco/HarperCollins) and the crime and mystery columnist for the New York Times Book Review. 
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Article reasoning-pattern comparisonThis article: 2.5%Sarah Weinman: 0.6%Foreign Policy: 3.4%Confirmation Bias2.5%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.4%Anchoring Bias0.0%This article: 4.1%Sarah Weinman: 2.8%Foreign Policy: 2.6%Availability Heuristic4.1%This article: 2.6%Sarah Weinman: 0.8%Foreign Policy: 0.9%Representativeness Heuristic2.6%This article: 1.9%Sarah Weinman: 0.5%Foreign Policy: 0.8%Hindsight Bias1.9%This article: 1.8%Sarah Weinman: 0.7%Foreign Policy: 2.3%Overconfidence Bias1.8%This article: 3.8%Sarah Weinman: 4.2%Foreign Policy: 4.9%Framing Effect3.8%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Loss Aversion0.0%This article: 1.3%Sarah Weinman: 0.3%Foreign Policy: 0.3%Status Quo Bias1.3%This article: 2.5%Sarah Weinman: 0.6%Foreign Policy: 0.1%Sunk Cost Effect2.5%This article: 0.8%Sarah Weinman: 0.2%Foreign Policy: 1.8%Optimism Bias0.8%This article: 7.5%Sarah Weinman: 3.6%Foreign Policy: 2.9%Pessimism Bias7.5%This article: 24.8%Sarah Weinman: 6.9%Foreign Policy: 7.9%Negativity Bias24.8%This article: 0.0%Sarah Weinman: 0.5%Foreign Policy: 0.5%Self-Serving Bias0.0%This article: 6.9%Sarah Weinman: 2.6%Foreign Policy: 1.2%Fundamental Attribution Error6.9%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Actor-Observer Bias0.0%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.7%In-Group Bias0.0%This article: 0.0%Sarah Weinman: 0.4%Foreign Policy: 0.6%Out-Group Homogeneity Bias0.0%This article: 2.2%Sarah Weinman: 3.3%Foreign Policy: 1.6%Halo Effect2.2%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.1%Horn Effect0.0%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.0%Dunning-Kruger Effect0.0%This article: 2.9%Sarah Weinman: 1.2%Foreign Policy: 1.4%Recency Bias2.9%This article: 3.3%Sarah Weinman: 0.8%Foreign Policy: 0.3%Primacy Effect3.3%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.0%Blind-Spot Bias0.0%This article: 2.6%Sarah Weinman: 0.7%Foreign Policy: 0.9%Ad Hominem2.6%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Straw Man0.0%This article: 6.0%Sarah Weinman: 2.9%Foreign Policy: 2.9%Appeal to Authority6.0%This article: 5.7%Sarah Weinman: 2.7%Foreign Policy: 1.7%False Dilemma5.7%This article: 6.2%Sarah Weinman: 2.3%Foreign Policy: 1.3%Slippery Slope6.2%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Circular Reasoning0.0%This article: 7.5%Sarah Weinman: 4.7%Foreign Policy: 4.8%Hasty Generalization7.5%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.1%Red Herring0.0%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Bandwagon0.0%This article: 6.3%Sarah Weinman: 2.2%Foreign Policy: 2.7%Appeal to Emotion6.3%This article: 0.3%Sarah Weinman: 0.1%Foreign Policy: 0.8%Begging the Question0.3%This article: 3.0%Sarah Weinman: 1.0%Foreign Policy: 3.1%Post Hoc (False Cause)3.0%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.1%Tu Quoque0.0%This article: 0.4%Sarah Weinman: 0.1%Foreign Policy: 0.3%Burden of Proof0.4%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.2%Appeal to Nature0.0%This article: 0.4%Sarah Weinman: 0.1%Foreign Policy: 0.3%Composition/Division0.4%This article: 7.3%Sarah Weinman: 2.0%Foreign Policy: 1.2%Anecdotal7.3%This article: 1.0%Sarah Weinman: 0.3%Foreign Policy: 0.1%No True Scotsman1.0%This article: 1.4%Sarah Weinman: 0.4%Foreign Policy: 1.6%Ambiguity (Equivocation)1.4%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.1%Middle Ground0.0%This article: 0.4%Sarah Weinman: 0.1%Foreign Policy: 0.0%Personal Incredulity0.4%This article: 1.3%Sarah Weinman: 0.3%Foreign Policy: 0.1%Special Pleading1.3%This article: 0.0%Sarah Weinman: 0.5%Foreign Policy: 0.1%Genetic Fallacy0.0%This article: 2.9%Sarah Weinman: 0.8%Foreign Policy: 1.8%Unattributed Quote2.9%This article: 1.2%Sarah Weinman: 0.5%Foreign Policy: 1.0%Quote-first Misdirection1.2%This article: 31.1%Sarah Weinman: 9.4%Foreign Policy: 5.5%Biased Writer Voice31.1%This article: 10.9%Sarah Weinman: 2.7%Foreign Policy: 1.1%Indoctrination10.9%This article: 1.3%Sarah Weinman: 0.3%Foreign Policy: 0.7%Politically Left Leaning Bias1.3%This article: 0.0%Sarah Weinman: 0.0%Foreign Policy: 0.3%Politically Right Leaning Bias0.0%This article: 2.7%Sarah Weinman: 1.3%Foreign Policy: 0.4%Attempt to Sell a Product or S…2.7%

2452 words analyzed.

Speakers

5speakers7.5%attributed speech2,268writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageSarah Weinman • 19 words • 100.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverageWriter's voice • 42 words • 100.0% coverageWriter's voice • 61 words • 0.0% coverageWriter's voice • 44 words • 100.0% coverageFederal Trade Commission • 44 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 38 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 36 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 42 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 43 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 57 words • 100.0% coverageWriter's voice • 58 words • 100.0% coverageWriter's voice • 40 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 42 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 44 words • 100.0% coverageWriter's voice • 47 words • 100.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 48 words • 100.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 41 words • 100.0% coverageCarlos Barrágan • 14 words • 0.0% coverageCarlos Barrágan • 19 words • 0.0% coverageCarlos Barrágan • 12 words • 0.0% coverageCarlos Barrágan • 5 words • 0.0% coverageCarlos Barrágan • 20 words • 100.0% coverageCarlos Barrágan • 17 words • 0.0% coverageCarlos Barrágan • 8 words • 0.0% coverageCarlos Barrágan • 9 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 26 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 15 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 41 words • 100.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 5 words • 100.0% coverageAlexandra de Hoop Scheffer • 4 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageUkraine’s ambassador to the United States • 13 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverage
100%flagged-word coverage
44 attributed words24% of attributed speech67% writer coverage
0%17.5%35.0%Biased Writer Voice-32.8 ptsWriter: 32.8%Federal Trade Commission: 0.0%0.0%Indoctrination-11.8 ptsWriter: 11.8%Federal Trade Commission: 0.0%0.0%Unattributed Quote-3.2 ptsWriter: 3.2%Federal Trade Commission: 0.0%0.0%Attempt to Sell a Product -2.1 ptsWriter: 2.1%Federal Trade Commission: 0.0%0.0%Politically Left Leaning B-1.4 ptsWriter: 1.4%Federal Trade Commission: 0.0%0.0%Quote-first Misdirection-1.3 ptsWriter: 1.3%Federal Trade Commission: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.