Trafficked, beaten and raped: raids reveal scale of abuse of women in Asia’s cyberscam centres 11%

By Fiona Kelliher0%

6/29/2026, 4:00:27 AM

BS Summary: This article contains 22 faulty reasoning types, including Representativeness Heuristic, Availability Heuristic, and Anecdotal, with Negativity Bias as the most egregious example at 14% saturation with 146 hits. Analysis detected 1,273 faulty-reasoning hits from 1,040 analyzed words, generating a BS Score of 27.3% and a BS Rank of 11% (19,042 of 21,176 articles). This article is better (less manipulative) than 89.90% of the article peer group.

Late one evening in October 2023, Sarah* felt labour pains starting. 
It was 11pm, but at the cyberscam compound inside Laos’ Golden Triangle, workers were logging on for a long night shift, scamming Americans online. 
Every night, workers sat at their computers until the early hours, building fake profiles of glamorous, jet-setting women on Facebook and Instagram. 
Sarah trawled the web to find older men to target with messages, where she fawned over their jobs, asked how their day had been and exchanged photos of luxury travel and beach trips. 
Each conversation she had was meticulously designed to follow a multi-day script, and monitored by bosses who walked up and down the long rows of desks. 
Eventually, Sarah would lead the conversation towards crypto investments and share fake screenshots of sizeable profits. 
The hope was that the person on the receiving end could be reeled in for a payout: a transfer of funds to an investment scheme that would eventually reveal itself to be fraudulent. 
A 39-year-old former shopkeeper from Uganda, Sarah was lured to Laos by the promise of a job as a social media manager, before being sold between three Golden Triangle compounds starting in 2022. 
A few months after arriving, she experienced several days of sexual abuse inside what workers called the “dark room”  separate quarters in which compound bosses doled out beatings and rapes. 
A group of men were forced to rape Sarah and three other women as a joint punishment after they refused to scam more victims. 
She had hidden her pregnancy, terrified the Chinese bosses running the compound would kill her if they found out. 
But now the baby was coming. 
Sarah grabbed the office’s shared smartphone and ran downstairs to the building’s entrance, where the guard was momentarily absent. 
Using Google translate, she asked a taxi driver to drive her to a hospital. 
“I can’t even believe it, because I just went out,” Sarah says, now in Kampala, as her two-year-old son plays on the floor next to her. 
“Maybe God helped me to go.” 
Like the hundreds of thousands of people who have been trafficked into south-east Asia’s scam compounds, Sarah’s day-to-day life inside the Golden Triangle consisted of forced labour, cramped living conditions and beatings. 
But she also experienced the additional trauma of sexual abuse, a common but overlooked reality reported by a growing number of women who have escaped the industry. 
Run primarily by Chinese and Taiwanese criminal syndicates, illicit cyberscamming has expanded across Laos, Myanmar and Cambodia since 2020, leading to estimated fraud losses of tens of billions of dollars. 
Like the men, female cyberscammers are expected to lure victims via chats  but they are also used to pose in fake social media profiles or speak over video calls. 
Experts have long understood the workforce to be overwhelmingly male. 
But as government-led raids in Cambodia and Myanmar have freed tens of thousands of workers in recent months, female survivors are increasingly sharing stories of gender-based violence that previously received little media or government attention. 
The Guardian spoke with six women, all former compound workers, who described gendered exploitation, including sexual attacks, lack of access to sanitary products and verbal abuse. 
Compound bosses use rape to punish women, they said, as well as a reward for men who successfully completed lucrative scams. 
When she failed to respond fast enough, the boss punched her in the head, kicked her and sexually abused her, she says. 
“If I’d had someone who could tell me, ‘Wherever you’re going, it’s not good’  I could have listened,” says Rachel, who supports her parents and young son. 
“But I had no idea about anything. 
I only wanted to go and work, and to earn for myself and for my family.” 
Not all women return home. 
Twenty-two-year-old Lintang*, a scam worker from Indonesia’s Riau province, was barely able to walk when she was admitted to a Cambodian hospital on 20 February. 
Lintang told an NGO case handler, who asked to remain anonymous for safety reasons, that she had been repeatedly gang-raped. 
Lintang was diagnosed with HIV and tuberculosis, according to the Indonesian embassy. 
Several NGOs tried to send Lintang to Indonesia for treatment, but it proved expensive and difficult. 
She died on 10 March. 
As Sarah laboured in the hospital early that October morning, the health staff demanded payment for her treatment. 
“I had nothing, not even clothes,” she says. 
But she did have a friend: Ketsana*, a Lao citizen now living just outside the Golden Triangle. 
At about 5am, Sarah texted Ketsana a photo of her newborn son. 
Bewildered, Ketsana “just asked whose child it was,” she says. 
A mother of two who had worked as a local government administrator, Ketsana was trafficked into a Golden Triangle scam operation in 2022 after being promised work as a housekeeper, then sold to two other compounds, before eventually being moved to the same building as Sarah. 
Because of the language barrier, the women only spoke a few times, but they exchanged Facebook details before Ketsana escaped during a police raid. 
Ketsana sent a taxi to pick up Sarah and her baby, and for the next month, the trio shared a two-room apartment. 
Sarah flew home to Uganda 40 days after her son was born with the help of an NGO. 
She has since trained to work as a tailor, earning about 7,000 Ugandan shillings (₤1.40) a day, but it is not enough to cover living expenses. 
She warns other women  often other single mothers  that they should think twice before travelling abroad to work, lest they return as “bodies”. 
On days when there is nothing to eat in the house, or no money for a doctor’s visit, a mix of emotions washes over Sarah: frustration over her finances, love for her son  and memories of “the many things I went through” in the Golden Triangle. 
“When he’s sleeping, when I don’t have anything to feed him  that’s when I think about it.” 
*Names have been changed to protect their identities. 
*Konlaphat Siri contributed to this report. 
*This reporting was supported by the Overseas Press Club Foundation. 
Article reasoning-pattern comparisonThis article: 2.7%Fiona Kelliher: 2.3%the Guardian: 3.7%Confirmation Bias2.7%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.6%Anchoring Bias0.0%This article: 12.3%Fiona Kelliher: 4.5%the Guardian: 3.1%Availability Heuristic12.3%This article: 12.5%Fiona Kelliher: 1.8%the Guardian: 1.2%Representativeness Heuristic12.5%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 1.0%Hindsight Bias0.0%This article: 2.9%Fiona Kelliher: 6.4%the Guardian: 1.6%Overconfidence Bias2.9%This article: 3.5%Fiona Kelliher: 8.3%the Guardian: 6.1%Framing Effect3.5%This article: 2.5%Fiona Kelliher: 0.7%the Guardian: 0.6%Loss Aversion2.5%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.6%Status Quo Bias0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.2%Sunk Cost Effect0.0%This article: 0.7%Fiona Kelliher: 6.0%the Guardian: 2.5%Optimism Bias0.7%This article: 4.7%Fiona Kelliher: 4.0%the Guardian: 2.1%Pessimism Bias4.7%This article: 14.0%Fiona Kelliher: 16.3%the Guardian: 10.4%Negativity Bias14.0%This article: 1.5%Fiona Kelliher: 3.1%the Guardian: 1.6%Self-Serving Bias1.5%This article: 3.2%Fiona Kelliher: 0.7%the Guardian: 1.4%Fundamental Attribution Error3.2%This article: 3.7%Fiona Kelliher: 1.0%the Guardian: 0.3%Actor-Observer Bias3.7%This article: 0.0%Fiona Kelliher: 1.2%the Guardian: 1.3%In-Group Bias0.0%This article: 0.0%Fiona Kelliher: 1.6%the Guardian: 0.7%Out-Group Homogeneity Bias0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 3.2%Halo Effect0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.3%Horn Effect0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.0%Dunning-Kruger Effect0.0%This article: 3.4%Fiona Kelliher: 1.7%the Guardian: 1.1%Recency Bias3.4%This article: 0.0%Fiona Kelliher: 0.2%the Guardian: 0.4%Primacy Effect0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Blind-Spot Bias0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 2.0%Ad Hominem0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.6%Straw Man0.0%This article: 6.0%Fiona Kelliher: 1.8%the Guardian: 3.8%Appeal to Authority6.0%This article: 0.0%Fiona Kelliher: 0.4%the Guardian: 1.7%False Dilemma0.0%This article: 0.0%Fiona Kelliher: 0.2%the Guardian: 1.1%Slippery Slope0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Circular Reasoning0.0%This article: 8.6%Fiona Kelliher: 3.8%the Guardian: 6.3%Hasty Generalization8.6%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.2%Red Herring0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.7%Bandwagon0.0%This article: 9.4%Fiona Kelliher: 6.4%the Guardian: 5.6%Appeal to Emotion9.4%This article: 0.0%Fiona Kelliher: 0.8%the Guardian: 0.9%Begging the Question0.0%This article: 3.9%Fiona Kelliher: 2.0%the Guardian: 2.9%Post Hoc (False Cause)3.9%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Tu Quoque0.0%This article: 0.0%Fiona Kelliher: 0.5%the Guardian: 0.4%Burden of Proof0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.2%Appeal to Nature0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.4%Composition/Division0.0%This article: 10.3%Fiona Kelliher: 2.9%the Guardian: 3.1%Anecdotal10.3%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%No True Scotsman0.0%This article: 3.0%Fiona Kelliher: 3.7%the Guardian: 1.6%Ambiguity (Equivocation)3.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Middle Ground0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Personal Incredulity0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.1%Special Pleading0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.3%Genetic Fallacy0.0%This article: 8.8%Fiona Kelliher: 7.0%the Guardian: 1.6%Unattributed Quote8.8%This article: 2.5%Fiona Kelliher: 1.6%the Guardian: 1.1%Quote-first Misdirection2.5%This article: 0.0%Fiona Kelliher: 3.4%the Guardian: 10.4%Biased Writer Voice0.0%This article: 2.4%Fiona Kelliher: 1.2%the Guardian: 1.8%Indoctrination2.4%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 3.1%Politically Left Leaning Bias0.0%This article: 0.0%Fiona Kelliher: 0.0%the Guardian: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Fiona Kelliher: 3.0%the Guardian: 1.0%Attempt to Sell a Product or S…0.0%

1040 words analyzed.

Speakers

4speakers15%attributed speech880writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 15 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageSarah • 26 words • 100.0% coverageSarah • 6 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageRachel • 28 words • 100.0% coverageRachel • 7 words • 0.0% coverageRachel • 16 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageSarah • 8 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageSarah • 10 words • 100.0% coverageWriter's voice • 46 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageSarah • 25 words • 100.0% coverageWriter's voice • 47 words • 0.0% coverageSarah • 18 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageKonlaphat Siri • 6 words • 0.0% coverageOverseas Press Club Foundation • 10 words • 0.0% coverage
Selected voice

Sarah

100%flagged-word coverage
93 attributed words58% of attributed speech68% writer coverage
0%25.0%50.0%Unattributed Quote+45.0 ptsWriter: 2.3%Sarah: 47.3%47.3%Quote-first Misdirection+28.0 ptsWriter: 0.0%Sarah: 28.0%28.0%Indoctrination+26.9 ptsWriter: 0.0%Sarah: 26.9%26.9%

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.