ZDNET69%

LinkedIn's new 'Seems like AI slop' button lets you report all those cringey posts 39%

By Jada Jones60%

7/31/2026, 9:42:21 AM

BS Summary: This article contains 20 faulty reasoning types, including Biased Writer Voice, Negativity Bias, and Appeal to Authority, with Hasty Generalization as the most egregious example at 21% saturation with 162 hits. Analysis detected 1,166 faulty-reasoning hits from 773 analyzed words, generating a BS Score of 38.7% and a BS Rank of 39% (16,335 of 26,610 articles). This article is better (less manipulative) than 61.40% of the article peer group.

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ZDNET's key takeaways 
LinkedIn announced a "Seems like AI slop" button. 
Users can report low-quality AI-written posts. 
AI slop posts are ubiquitous on the internet. 
As LinkedIn has solidified itself as a corporate influencer site-slash-job-hunting search engine, there's something that irritates users more than the cog-in-the-corporate-machine posters themselves: AI slop. 
Someone mentioning that the most important executive-level lessons aren't discovered in a boardroom -- but in some other innocuous and unrelated setting. 
A callout to today's "fast-paced world." 
Several short, jab-like sentences. 
Dramatic line breaks. 
Wrapping up the post by encouraging the reader to "let that sink in." 
And honestly? 
The entire post ends up being a big, fat nothingburger. 
Also: OpenAI's rogue agent didn't stop at Hugging Face - here's what we know 
Do you recognize this prose? 
AI-written text posts are all over the internet, and LinkedIn is a breeding ground for them. 
An Originality.ai study found that 81% of long-form posts on LinkedIn are likely AI-generated. 
Users are growing frustrated, and LinkedIn is vowing to do something about it. 
What is AI slop? 
According to Merriam-Webster, AI slop (noun) is "digital content of low quality that is produced usually in quantity by means of artificial intelligence." 
The term has become so popular that this definition is the first in the updated dictionary under "slop," and the Associated Press created an entry for it in the latest edition of its writing and editing guide for journalists. 
Also: What the FCC ban on foreign-made robot vacuums means for your Roomba 
AI slop can refer to thoughtless, mass-produced AI-generated images, music, videos, and text posts whose creators hope to game an algorithm, generate views and clicks, and make money. 
Are you familiar with "Fruit Love Island"? 
How LinkedIn is fighting AI slop 
Hari Srinivasan, chief product officer at LinkedIn, posted on the site detailing how the company is combating swaths of AI-generated content. 
In his post, Srinivasan said the platform has invested in its automated defenses, especially in the comments section. 
Srinivasan's post also said that the platform is introducing new classifiers to identify low-quality content. 
However, Srinivasan acknowledged that not all AI-generated content is slop, as some people use AI to refine their raw thoughts, and more users can recognize when AI-generated posts are made in good faith or are mindless and disingenuous. 
Also: 74% of workers ask AI questions instead of colleagues - with potentially serious consequences 
Though LinkedIn is deploying automated models to recognize slop posts, Srinivasan said real user feedback is more valuable, and encouraged users to use the new "Seems like AI slop" button to report suspected posts. 
People who create posts will be able to privately see in their dashboards when their audience members feel their posts are inauthentic and slop-adjacent. 
As a result, users should see fewer slop-like posts in their suggested content feed from users outside of their networks, and posts suspected of being slop will have a smaller reach. 
Why does LinkedIn attract so much AI slop? 
LinkedIn's posting culture can sometimes reward performative "hustle culture" and constant "C-suite level thought leadership" posts, which creates a byproduct of some people posting hollow, low-effort content on these topics to stay relevant and visible. 
Also: AI agents are your new colleagues - how to get the best results 
According to Hootsuite, LinkedIn's algorithm rewards topic relevance, post engagement, and topic consistency. 
So, someone who makes two to three AI-generated posts a day about "C-suite level thought leadership" with hundreds of AI-generated comments underneath it will experience wider reach and increased engagement than someone who has real thoughts to share on the same topic two to three times a month. 
LinkedIn isn't the only online platform looking to decrease the amount of AI slop users encounter. 
Substack, a digital writing and newsletter tool, announced a partnership with Panagram, an AI detection company, to show readers how much of a Substack post may be AI-generated. 
Also: I gave Perplexity's agentic AI 5 complex tasks to run on my Mac - and I'll do it again 
Several threads in Reddit communities parrot a growing annoyance with AI-sloppy text posts online. 
These posts are everywhere, from book communities to a women's golf subreddit. 
Perhaps no one predicted that ChatGPT would be used to engagement farm all over the internet at its inception in 2023. 
Yet, chatbot "speech" patterns are all over the internet now. 
AI slop posts may not be going anywhere anytime soon, but digital platforms are taking steps to curb their reach and popularity. 
Article reasoning-pattern comparisonThis article: 4.9%Jada Jones: 3.1%ZDNET: 2.6%Confirmation Bias4.9%This article: 0.0%Jada Jones: 0.1%ZDNET: 1.3%Anchoring Bias0.0%This article: 9.8%Jada Jones: 3.9%ZDNET: 2.8%Availability Heuristic9.8%This article: 0.0%Jada Jones: 0.3%ZDNET: 0.9%Representativeness Heuristic0.0%This article: 2.7%Jada Jones: 0.5%ZDNET: 0.5%Hindsight Bias2.7%This article: 2.3%Jada Jones: 1.2%ZDNET: 2.7%Overconfidence Bias2.3%This article: 0.0%Jada Jones: 2.3%ZDNET: 3.4%Framing Effect0.0%This article: 0.0%Jada Jones: 0.9%ZDNET: 1.1%Loss Aversion0.0%This article: 0.0%Jada Jones: 0.3%ZDNET: 0.6%Status Quo Bias0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.2%Sunk Cost Effect0.0%This article: 11.5%Jada Jones: 1.7%ZDNET: 4.1%Optimism Bias11.5%This article: 2.8%Jada Jones: 1.4%ZDNET: 1.3%Pessimism Bias2.8%This article: 15.4%Jada Jones: 3.3%ZDNET: 4.6%Negativity Bias15.4%This article: 0.0%Jada Jones: 0.4%ZDNET: 1.3%Self-Serving Bias0.0%This article: 13.1%Jada Jones: 1.6%ZDNET: 0.3%Fundamental Attribution Error13.1%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%Actor-Observer Bias0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.4%In-Group Bias0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Jada Jones: 4.9%ZDNET: 2.9%Halo Effect0.0%This article: 0.0%Jada Jones: 0.3%ZDNET: 0.1%Horn Effect0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Jada Jones: 0.1%ZDNET: 1.2%Recency Bias0.0%This article: 0.6%Jada Jones: 0.2%ZDNET: 0.3%Primacy Effect0.6%This article: 0.0%Jada Jones: 0.6%ZDNET: 0.1%Blind-Spot Bias0.0%This article: 1.8%Jada Jones: 0.1%ZDNET: 0.0%Ad Hominem1.8%This article: 3.2%Jada Jones: 0.2%ZDNET: 0.1%Straw Man3.2%This article: 14.0%Jada Jones: 3.6%ZDNET: 3.9%Appeal to Authority14.0%This article: 0.0%Jada Jones: 0.3%ZDNET: 1.2%False Dilemma0.0%This article: 2.8%Jada Jones: 0.4%ZDNET: 0.6%Slippery Slope2.8%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.2%Circular Reasoning0.0%This article: 21.0%Jada Jones: 4.9%ZDNET: 5.5%Hasty Generalization21.0%This article: 0.0%Jada Jones: 0.7%ZDNET: 0.4%Red Herring0.0%This article: 0.0%Jada Jones: 0.1%ZDNET: 0.5%Bandwagon0.0%This article: 0.0%Jada Jones: 0.7%ZDNET: 1.7%Appeal to Emotion0.0%This article: 0.0%Jada Jones: 0.1%ZDNET: 0.5%Begging the Question0.0%This article: 11.6%Jada Jones: 0.7%ZDNET: 1.4%Post Hoc (False Cause)11.6%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.0%Tu Quoque0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.2%Burden of Proof0.0%This article: 0.0%Jada Jones: 0.3%ZDNET: 0.1%Appeal to Nature0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.2%Composition/Division0.0%This article: 6.3%Jada Jones: 1.8%ZDNET: 4.4%Anecdotal6.3%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%No True Scotsman0.0%This article: 2.7%Jada Jones: 1.0%ZDNET: 1.9%Ambiguity (Equivocation)2.7%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%Middle Ground0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%Personal Incredulity0.0%This article: 0.0%Jada Jones: 0.3%ZDNET: 0.1%Special Pleading0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.1%Genetic Fallacy0.0%This article: 0.0%Jada Jones: 0.1%ZDNET: 1.0%Unattributed Quote0.0%This article: 0.6%Jada Jones: 0.0%ZDNET: 0.4%Quote-first Misdirection0.6%This article: 20.3%Jada Jones: 2.3%ZDNET: 5.4%Biased Writer Voice20.3%This article: 0.0%Jada Jones: 1.9%ZDNET: 3.0%Indoctrination0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Jada Jones: 0.0%ZDNET: 0.0%Politically Right Leaning Bias0.0%This article: 3.1%Jada Jones: 7.2%ZDNET: 6.2%Attempt to Sell a Product or S…3.1%

773 words analyzed.

Speakers

2speakers18%attributed speech634writer words
Selected voice

Hootsuite

100%flagged-word coverage
13 attributed words9.4% of attributed speech74% writer coverage
0%12.5%25.0%Biased Writer Voice-24.8 ptsWriter: 24.8%Hootsuite: 0.0%0.0%Attempt to Sell a Product -3.8 ptsWriter: 3.8%Hootsuite: 0.0%0.0%Quote-first Misdirection-0.8 ptsWriter: 0.8%Hootsuite: 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.