MS NOW⁠95%

Mike Lindell loses bid to overturn defamation verdict. Plus, his lawyers face sanctions. ⁠4%

By Jordan Rubin⁠0%

3/26/2026, 9:13:27 PM

BS Summary: This article contains 7 faulty reasoning types, including Appeal to Authority, Burden of Proof, and Loss Aversion, with Negativity Bias as the most egregious example at 14.9% saturation with 78 hits. Analysis detected 247 faulty-reasoning hits from 525 analyzed words, generating a BS Score of 17.3% and a BS Rank of ⁠4% (21,121 of 21,886 articles). This article is better (less manipulative) than 96.50% of the article peer group.

MyPillow CEO Mike Lindell not only lost his latest motion to overturn a defamation verdict related to the 2020 election — the judge also said lawyers for the media company he founded must explain why they shouldn’t be fined and referred to their state bars for disciplinary proceedings. 
The double defeat came Wednesday from U.S. 
District Judge Nina Wang, a Joe Biden appointee in Colorado. 
She presided over the civil case in which a jury found the Minnesota gubernatorial hopeful and his media company, Frankspeech, liable for defaming Eric Coomer. 
The plaintiff had worked for Dominion Voting Systems, a voting technology company targeted by Donald Trump supporters in connection with the election he lost to Biden. 
Wang rejected the post-trial motion challenging the verdict, which totaled over $2 million against Lindell and Frankspeech. 
Through that company, Lindell broadcast his show, which included statements about Coomer that led to this lawsuit. 
In her ruling, the judge recounted Coomer testifying that Lindell’s statements accusing him of election-rigging made the plaintiff unlikely to ever work in anything election-related again. 
To be sure, the decision wasn’t a total loss for the Lindell crew, as the judge also rejected the plaintiff’s motion to increase the damages. 
Yet the actions of Frankspeech’s lawyers in defending against that motion led the judge to raise ethical concerns. 
Wang noted that, in their response to the plaintiff’s motion to increase the damages, defense lawyers Christopher Kachouroff and Jennifer DeMaster cited a case as having been decided by a federal appeals court. 
But the judge observed that it was actually a trial-level ruling that the lawyers misleadingly described, to boot. 
“A reasonable review by counsel should have alerted them of the error,” Wang wrote. 
Incredibly, this sort of thing wasn’t a one-off. 
The judge pointed out that she had already sanctioned the defense lawyers for “this exact type of error,” which the lawyers had said then was due to the use of artificial intelligence and mistakenly filing the wrong draft. 
“The Court cannot ignore this reoccurring conduct simply because the trial is over,” Wang wrote Wednesday. 
“Regardless of whether generative artificial intelligence was used or not,” she wrote, the federal appeals court covering Colorado “has been clear that an attorney has a ‘fundamental duty’ to the Court to confirm that all legal authorities in submissions to the Court are accurately cited, reflect accurate quotations, and stand for the propositions for which they are cited.” 
The judge bolded the phrase “fundamental duty.”. 
She deemed it “inexplicable” how these errors happened “yet again” — she bolded that phrase, too — after previously reminding the lawyers of their professional obligations. 
Recalling that she had sanctioned them $3,000 each, she wrote Wednesday that her actions thus far seemed to have had “little, if any, remedial impact.” 
The judge therefore gave Kachouroff and DeMaster until April 8 to convince her why they shouldn’t be sanctioned further “for their continued failure to check their citations” as required by court rules, and why they shouldn’t be referred to their state bars in Virginia and Wisconsin, respectively, for disciplinary proceedings. 
Article reasoning-pattern comparisonThis article: 0.0%Jordan Rubin: 8.3%MS NOW: 7.5%Confirmation Bias0.0%This article: 0.0%Jordan Rubin: 2.5%MS NOW: 1.2%Anchoring Bias0.0%This article: 0.0%Jordan Rubin: 2.8%MS NOW: 3.7%Availability Heuristic0.0%This article: 0.0%Jordan Rubin: 0.6%MS NOW: 1.1%Representativeness Heuristic0.0%This article: 0.0%Jordan Rubin: 0.9%MS NOW: 1.3%Hindsight Bias0.0%This article: 0.0%Jordan Rubin: 2.0%MS NOW: 2.6%Overconfidence Bias0.0%This article: 1.9%Jordan Rubin: 21.5%MS NOW: 15.4%Framing Effect1.9%This article: 4.8%Jordan Rubin: 2.0%MS NOW: 0.7%Loss Aversion4.8%This article: 0.0%Jordan Rubin: 2.2%MS NOW: 0.9%Status Quo Bias0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.1%Sunk Cost Effect0.0%This article: 0.0%Jordan Rubin: 0.8%MS NOW: 2.4%Optimism Bias0.0%This article: 0.0%Jordan Rubin: 2.7%MS NOW: 3.2%Pessimism Bias0.0%This article: 14.9%Jordan Rubin: 14.8%MS NOW: 19.2%Negativity Bias14.9%This article: 0.0%Jordan Rubin: 0.7%MS NOW: 2.4%Self-Serving Bias0.0%This article: 0.0%Jordan Rubin: 1.4%MS NOW: 3.1%Fundamental Attribution Error0.0%This article: 0.0%Jordan Rubin: 0.2%MS NOW: 0.3%Actor-Observer Bias0.0%This article: 0.0%Jordan Rubin: 2.7%MS NOW: 3.5%In-Group Bias0.0%This article: 0.0%Jordan Rubin: 2.1%MS NOW: 2.5%Out-Group Homogeneity Bias0.0%This article: 0.0%Jordan Rubin: 0.7%MS NOW: 2.2%Halo Effect0.0%This article: 0.0%Jordan Rubin: 0.2%MS NOW: 1.8%Horn Effect0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Jordan Rubin: 1.7%MS NOW: 1.8%Recency Bias0.0%This article: 0.0%Jordan Rubin: 0.7%MS NOW: 0.6%Primacy Effect0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.2%Blind-Spot Bias0.0%This article: 0.0%Jordan Rubin: 2.5%MS NOW: 4.7%Ad Hominem0.0%This article: 0.0%Jordan Rubin: 0.5%MS NOW: 1.3%Straw Man0.0%This article: 11.0%Jordan Rubin: 4.6%MS NOW: 5.4%Appeal to Authority11.0%This article: 0.0%Jordan Rubin: 2.2%MS NOW: 2.2%False Dilemma0.0%This article: 0.0%Jordan Rubin: 2.4%MS NOW: 2.2%Slippery Slope0.0%This article: 0.0%Jordan Rubin: 0.1%MS NOW: 0.2%Circular Reasoning0.0%This article: 0.0%Jordan Rubin: 3.4%MS NOW: 8.1%Hasty Generalization0.0%This article: 0.0%Jordan Rubin: 0.4%MS NOW: 0.7%Red Herring0.0%This article: 0.0%Jordan Rubin: 1.0%MS NOW: 0.9%Bandwagon0.0%This article: 0.0%Jordan Rubin: 7.4%MS NOW: 9.8%Appeal to Emotion0.0%This article: 0.0%Jordan Rubin: 2.0%MS NOW: 2.4%Begging the Question0.0%This article: 3.4%Jordan Rubin: 1.1%MS NOW: 3.2%Post Hoc (False Cause)3.4%This article: 0.0%Jordan Rubin: 0.6%MS NOW: 0.6%Tu Quoque0.0%This article: 9.5%Jordan Rubin: 0.7%MS NOW: 0.9%Burden of Proof9.5%This article: 0.0%Jordan Rubin: 0.1%MS NOW: 0.1%Appeal to Nature0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.3%Composition/Division0.0%This article: 0.0%Jordan Rubin: 1.3%MS NOW: 2.6%Anecdotal0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.2%No True Scotsman0.0%This article: 0.0%Jordan Rubin: 1.9%MS NOW: 1.7%Ambiguity (Equivocation)0.0%This article: 0.0%Jordan Rubin: 0.1%MS NOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Jordan Rubin: 0.1%MS NOW: 0.1%Middle Ground0.0%This article: 0.0%Jordan Rubin: 0.3%MS NOW: 0.3%Personal Incredulity0.0%This article: 0.0%Jordan Rubin: 0.0%MS NOW: 0.3%Special Pleading0.0%This article: 0.0%Jordan Rubin: 2.6%MS NOW: 1.3%Genetic Fallacy0.0%This article: 0.0%Jordan Rubin: 2.0%MS NOW: 2.5%Unattributed Quote0.0%This article: 0.0%Jordan Rubin: 2.2%MS NOW: 1.5%Quote-first Misdirection0.0%This article: 1.5%Jordan Rubin: 8.4%MS NOW: 14.2%Biased Writer Voice1.5%This article: 0.0%Jordan Rubin: 0.4%MS NOW: 2.1%Indoctrination0.0%This article: 0.0%Jordan Rubin: 5.5%MS NOW: 5.3%Politically Left Leaning Bias0.0%This article: 0.0%Jordan Rubin: 0.7%MS NOW: 0.6%Politically Right Leaning Bias0.0%This article: 0.0%Jordan Rubin: 1.0%MS NOW: 0.4%Attempt to Sell a Product or S…0.0%

525 words analyzed.

Speakers

1speaker17%attributed speech437writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 48 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageNina Wang • 14 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 38 words • 0.0% coverageNina Wang • 16 words • 0.0% coverageNina Wang • 58 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 50 words • 0.0% coverage
Selected voice

Nina Wang

66%flagged-word coverage
88 attributed words100% of attributed speech41% writer coverage
0%2.5%5.0%Biased Writer Voice-1.8 ptsWriter: 1.8%Nina Wang: 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.