Background checks to curb dating app violence advance in California legislature 7%

By Lynn La25%

4/17/2026, 2:00:00 PM

BS Summary: This article contains 8 faulty reasoning types, including Availability Heuristic, Appeal to Emotion, and Hasty Generalization, with Negativity Bias as the most egregious example at 26.6% saturation with 121 hits. Analysis detected 444 faulty-reasoning hits from 455 analyzed words, generating a BS Score of 22.7% and a BS Rank of 7% (20,518 of 21,886 articles). This article is better (less manipulative) than 93.70% of the article peer group.

A California bill to protect people on online dating apps from violence has critics arguing that the measure would put a “scarlet letter” on certain users. 
But that’s a feature, not a bug in the proposal. 
The state Senate’s public safety committee this week passed a bill that would require online dating services to run criminal background checks on California users. 
If the user is a registered sex offender or has been convicted of a violent felony, domestic violence, an assault or a hate crime, the dating service must “place a flag” on the user’s profile to let others know. 
Dating App Cover-Up: How Tinder, Hinge, and Their Corporate Owner Keep Rape Under Wraps 
The company behind more than a dozen dating apps, Match Group, has known for years about the abusive users on its platforms, but chooses to leave millions of people in the dark 
February 13, 2025 5:00 a.m. 
UTC 
Bill author, state Sen. 
Caroline Menjivar, said that “dating apps have not provided an adequate level of safety for their users.” 
At the hearing she cited a 2019 Columbia Journalism Investigations survey that found that more than a third of women polled said they were sexually assaulted or raped by someone they met on a dating app. 
The Markup last year published an investigation that showed people accused of sexual violence managed to stay on the apps even after victims reported them. 
Menjivar, a Van Nuys Democrat, added: “If women, mostly women, continue to be raped or murdered  like another woman (who) was murdered and her body was set on fire last year after a man met her on a dating app  those are the incidents we’re looking to prevent.” 
But besides labeling users with a “scarlet letter,” implementing the policy would require dating platforms to collect a significant amount of personal data to avoid misidentifying users, argued Jose Torres, a deputy executive director for the industry group TechNet. 
In a rare break with his Democratic colleagues, Sen. 
Scott Wiener of San Francisco voted against the bill, saying it might have “significant unintended consequences in terms of people’s privacy.” 
But Wiener’s opposition, along with Republican Sen. 
Kelly Seyarto of Murrieta, was not enough to stop the bill from advancing out of the six-member committee, according to the Digital Democracy database from CalMatters, of which The Markup is a part. 
With four Democratic lawmakers giving the green light, Menjivar said she plans to amend the measure in response to criticism related to the categories of crime and operational challenges  and that legislators are “going to see a dramatically different bill” when it’s presented to the privacy committee on April 20. 
Article reasoning-pattern comparisonThis article: 0.0%Lynn La: 1.4%The Markup: 2.2%Confirmation Bias0.0%This article: 0.0%Lynn La: 0.6%The Markup: 0.2%Anchoring Bias0.0%This article: 18.9%Lynn La: 2.7%The Markup: 2.8%Availability Heuristic18.9%This article: 0.0%Lynn La: 0.7%The Markup: 0.7%Representativeness Heuristic0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.2%Hindsight Bias0.0%This article: 0.0%Lynn La: 0.3%The Markup: 1.7%Overconfidence Bias0.0%This article: 2.2%Lynn La: 6.4%The Markup: 3.2%Framing Effect2.2%This article: 0.0%Lynn La: 0.9%The Markup: 1.0%Loss Aversion0.0%This article: 0.0%Lynn La: 0.6%The Markup: 0.3%Status Quo Bias0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.0%Sunk Cost Effect0.0%This article: 0.0%Lynn La: 1.2%The Markup: 2.1%Optimism Bias0.0%This article: 0.0%Lynn La: 1.7%The Markup: 0.7%Pessimism Bias0.0%This article: 26.6%Lynn La: 9.0%The Markup: 6.5%Negativity Bias26.6%This article: 0.0%Lynn La: 1.9%The Markup: 0.7%Self-Serving Bias0.0%This article: 0.0%Lynn La: 1.2%The Markup: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Lynn La: 0.1%The Markup: 0.1%Actor-Observer Bias0.0%This article: 0.0%Lynn La: 1.1%The Markup: 0.5%In-Group Bias0.0%This article: 0.0%Lynn La: 0.5%The Markup: 0.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Lynn La: 0.8%The Markup: 2.5%Halo Effect0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.0%Horn Effect0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Lynn La: 0.6%The Markup: 1.1%Recency Bias0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.1%Primacy Effect0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.3%Blind-Spot Bias0.0%This article: 0.0%Lynn La: 0.9%The Markup: 0.1%Ad Hominem0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.1%Straw Man0.0%This article: 0.0%Lynn La: 3.7%The Markup: 3.8%Appeal to Authority0.0%This article: 0.0%Lynn La: 1.1%The Markup: 1.3%False Dilemma0.0%This article: 0.0%Lynn La: 0.8%The Markup: 0.3%Slippery Slope0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.0%Circular Reasoning0.0%This article: 12.5%Lynn La: 3.7%The Markup: 3.9%Hasty Generalization12.5%This article: 0.0%Lynn La: 0.1%The Markup: 0.2%Red Herring0.0%This article: 0.0%Lynn La: 0.5%The Markup: 0.4%Bandwagon0.0%This article: 14.1%Lynn La: 6.6%The Markup: 3.3%Appeal to Emotion14.1%This article: 0.0%Lynn La: 0.8%The Markup: 0.5%Begging the Question0.0%This article: 0.0%Lynn La: 2.1%The Markup: 1.7%Post Hoc (False Cause)0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.1%Tu Quoque0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.4%Burden of Proof0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.1%Appeal to Nature0.0%This article: 0.0%Lynn La: 0.1%The Markup: 0.0%Composition/Division0.0%This article: 11.0%Lynn La: 2.1%The Markup: 3.0%Anecdotal11.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.1%No True Scotsman0.0%This article: 0.0%Lynn La: 1.2%The Markup: 1.5%Ambiguity (Equivocation)0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.1%Middle Ground0.0%This article: 0.0%Lynn La: 0.0%The Markup: 0.1%Personal Incredulity0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.0%Special Pleading0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.1%Genetic Fallacy0.0%This article: 0.0%Lynn La: 2.2%The Markup: 0.6%Unattributed Quote0.0%This article: 3.1%Lynn La: 1.6%The Markup: 1.0%Quote-first Misdirection3.1%This article: 9.2%Lynn La: 4.2%The Markup: 2.3%Biased Writer Voice9.2%This article: 0.0%Lynn La: 2.0%The Markup: 1.2%Indoctrination0.0%This article: 0.0%Lynn La: 0.7%The Markup: 0.3%Politically Left Leaning Bias0.0%This article: 0.0%Lynn La: 0.2%The Markup: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Lynn La: 3.7%The Markup: 1.0%Attempt to Sell a Product or S…0.0%

455 words analyzed.

Speakers

3speakers47%attributed speech241writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 39 words • 0.0% coverageWriter's voice • 14 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageCaroline Menjivar • 17 words • 0.0% coverageCaroline Menjivar • 36 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageCaroline Menjivar • 50 words • 0.0% coverageJose Torres • 39 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageScott Wiener • 21 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageCaroline Menjivar • 51 words • 0.0% coverage
Selected voice

Caroline Menjivar

56%flagged-word coverage
154 attributed words72% of attributed speech34% writer coverage
0%10.0%20.0%Biased Writer Voice-17.4 ptsWriter: 17.4%Caroline Menjivar: 0.0%0.0%Quote-first Misdirection-5.8 ptsWriter: 5.8%Caroline Menjivar: 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.