Mainers losing millions to online scams as federal oversight fades 73%

By Kathryn Carley86% Maine News Service86%

7/31/2026, 7:02:51 AM

BS Summary: This article contains 20 faulty reasoning types, including Negativity Bias, Availability Heuristic, and Politically Left Leaning Bias, with Indoctrination as the most egregious example at 44.8% saturation with 175 hits. Analysis detected 914 faulty-reasoning hits from 391 analyzed words, generating a BS Score of 58.7% and a BS Rank of 73% (7,341 of 26,881 articles). This article is worse (more manipulative) than 72.70% of the article peer group.

Online scams and other crimes against Mainers skyrocketed last year after the Trump administration cut the budget for consumer and financial protections in half. 
A new report from the Consumer Federation of America shows Maine lost roughly $400 million, or more than $280 per person, with more than half of reported losses involving cryptocurrency. 
Ben Winters, director of AI and privacy for the federation, said a scam economy is thriving online and tech companies like Meta are profiting . 
“We’re at a circumstance where there is not enough enforcement, there is not enough wins against these scammers and there is all these tools that are making it easier and easier that are not getting shut down or prohibited,” Winters outlined. 
The vast majority of scam attempts occur on social media platforms via a direct message. 
Winters argued the crisis requires bold action by the federal government to hold tech companies accountable and better educate the public regarding online scams and safety. 
Federal legislation introduced as the SCAM Act aims to address the problem. 
The bill would ban paid scam ads online and require real advertiser verification and strict timelines for tech companies to investigate and remove fraudulent ads. 
It would also empower the Federal Trade Commission and states with greater enforcement abilities. 
Winters noted AI-generated content is becoming easier to make and harder to detect. 
Winters advised people to think twice before clicking on any ad or link involving a financial transaction. 
“Make sure to always pause when someone is asking you to do something quickly, to wire money quickly, to do something that seems a little bit off,” Winters recommended. 
“You can always hang up. 
You can always wait before you respond to an email or a message.” 
Adults age 60 and over remain the largest targeted demographic for scams. 
Reported losses have increased more than 60% since 2025, with the average scam costing people more than $38,000. 
Winters stressed it is important for victims of internet crime to contact the state attorney general or local police, adding there is no shame in falling victim to a scam but it is good to talk about it so others can be made aware. 
The post Mainers losing millions to online scams as federal oversight fades first appeared on Maine Beacon . 
Article reasoning-pattern comparisonThis article: 6.6%Kathryn Carley: 1.9%Maine Beacon: 5.6%Confirmation Bias6.6%This article: 7.7%Kathryn Carley: 1.5%Maine Beacon: 0.6%Anchoring Bias7.7%This article: 18.9%Kathryn Carley: 5.4%Maine Beacon: 3.3%Availability Heuristic18.9%This article: 3.1%Kathryn Carley: 0.6%Maine Beacon: 0.9%Representativeness Heuristic3.1%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.8%Hindsight Bias0.0%This article: 13.8%Kathryn Carley: 1.4%Maine Beacon: 1.4%Overconfidence Bias13.8%This article: 0.0%Kathryn Carley: 5.7%Maine Beacon: 9.6%Framing Effect0.0%This article: 15.1%Kathryn Carley: 2.5%Maine Beacon: 0.6%Loss Aversion15.1%This article: 10.0%Kathryn Carley: 1.6%Maine Beacon: 0.5%Status Quo Bias10.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.1%Sunk Cost Effect0.0%This article: 4.6%Kathryn Carley: 0.5%Maine Beacon: 2.4%Optimism Bias4.6%This article: 13.8%Kathryn Carley: 3.0%Maine Beacon: 1.7%Pessimism Bias13.8%This article: 24.3%Kathryn Carley: 17.0%Maine Beacon: 10.3%Negativity Bias24.3%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.9%Self-Serving Bias0.0%This article: 6.4%Kathryn Carley: 0.6%Maine Beacon: 1.0%Fundamental Attribution Error6.4%This article: 0.0%Kathryn Carley: 0.6%Maine Beacon: 0.1%Actor-Observer Bias0.0%This article: 0.0%Kathryn Carley: 0.3%Maine Beacon: 4.2%In-Group Bias0.0%This article: 0.0%Kathryn Carley: 0.6%Maine Beacon: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 1.7%Halo Effect0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.3%Horn Effect0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Dunning-Kruger Effect0.0%This article: 7.9%Kathryn Carley: 2.2%Maine Beacon: 1.5%Recency Bias7.9%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.6%Primacy Effect0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Blind-Spot Bias0.0%This article: 0.0%Kathryn Carley: 0.4%Maine Beacon: 2.3%Ad Hominem0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.7%Straw Man0.0%This article: 6.4%Kathryn Carley: 1.9%Maine Beacon: 2.0%Appeal to Authority6.4%This article: 6.6%Kathryn Carley: 1.1%Maine Beacon: 2.7%False Dilemma6.6%This article: 10.5%Kathryn Carley: 2.2%Maine Beacon: 1.2%Slippery Slope10.5%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.2%Circular Reasoning0.0%This article: 6.9%Kathryn Carley: 7.8%Maine Beacon: 6.3%Hasty Generalization6.9%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.3%Red Herring0.0%This article: 0.0%Kathryn Carley: 0.3%Maine Beacon: 1.7%Bandwagon0.0%This article: 0.0%Kathryn Carley: 10.9%Maine Beacon: 13.9%Appeal to Emotion0.0%This article: 0.0%Kathryn Carley: 1.7%Maine Beacon: 1.2%Begging the Question0.0%This article: 6.1%Kathryn Carley: 2.8%Maine Beacon: 3.1%Post Hoc (False Cause)6.1%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.3%Tu Quoque0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.5%Burden of Proof0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Appeal to Nature0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.4%Composition/Division0.0%This article: 0.0%Kathryn Carley: 5.2%Maine Beacon: 3.5%Anecdotal0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.1%No True Scotsman0.0%This article: 0.0%Kathryn Carley: 0.6%Maine Beacon: 0.7%Ambiguity (Equivocation)0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Middle Ground0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Personal Incredulity0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.1%Special Pleading0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.0%Genetic Fallacy0.0%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 1.2%Unattributed Quote0.0%This article: 0.0%Kathryn Carley: 0.4%Maine Beacon: 0.9%Quote-first Misdirection0.0%This article: 2.6%Kathryn Carley: 2.4%Maine Beacon: 8.2%Biased Writer Voice2.6%This article: 44.8%Kathryn Carley: 13.1%Maine Beacon: 6.2%Indoctrination44.8%This article: 17.6%Kathryn Carley: 6.3%Maine Beacon: 10.9%Politically Left Leaning Bias17.6%This article: 0.0%Kathryn Carley: 0.0%Maine Beacon: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Kathryn Carley: 1.0%Maine Beacon: 0.6%Attempt to Sell a Product or S…0.0%

391 words analyzed.

Speakers

2speakers62%attributed speech148writer words
100%flagged-word coverage
30 attributed words12% of attributed speech80% writer coverage
0%50.0%100.0%Politically Left Leaning B+73.6 ptsWriter: 26.4%Consumer Federation of America: 100.0%100.0%Biased Writer Voice-6.8 ptsWriter: 6.8%Consumer Federation of America: 0.0%0.0%

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

Loading…
Loading…
Loading…
Loading…

Analysis

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