{"slug": "linkedin-adds-ai-slop-reporting-option", "title": "LinkedIn Adds AI Slop Reporting Option", "summary": "LinkedIn added a 'Seems like AI slop' reporting option on July 30, 2026, allowing users to flag suspected low-effort AI-generated posts, according to 404 Media's testing. Selecting the option hides the reported post and returns a message saying the feedback helps improve the feed. The feature follows LinkedIn's earlier effort to reduce recommendations for generic, repetitive, and engagement-bait content, Engadget reported.", "body_md": "# LinkedIn Adds AI Slop Reporting Option\n\nLinkedIn added a \"Seems like AI slop\" reporting option on July 30, 2026, allowing users to flag suspected low-effort AI-generated posts, according to 404 Media's testing. Selecting the option hides the reported post and returns a message saying the feedback helps improve the feed. The feature follows LinkedIn's earlier effort to reduce recommendations for generic, repetitive, and engagement-bait content, Engadget reported.\n\nLinkedIn has added a \"Seems like AI slop\" option to its post-reporting menu, allowing users to flag posts they believe are AI-generated and low effort. In testing reported by 404 Media on July 30, selecting the option hid the post and displayed the message: \"Thanks for letting us know. Your feedback helps improve the feed.\"\n\nThe option appears in the menu opened from the three-dot control on a post, according to 404 Media. The outlet reported that the feedback mechanism will partly help LinkedIn tune its models for identifying AI slop.\n\nAfter 404 Media published its report, LinkedIn Chief Product Officer Hari Srinivasan wrote that \"AI slop is a top priority for all of us. We really care about this. People come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise.\" The report also quoted Srinivasan as saying LinkedIn was ramping up new and improved classifiers.\n\n### Broader feed-ranking effort\n\nThe reporting follows a broader LinkedIn content-quality initiative covered by Engadget in May. Engadget reported that LinkedIn had begun reducing the recommendation reach of engagement bait, recycled thought leadership, and generic content lacking authenticity or originality. Under that approach, identified posts could remain visible to direct connections and followers while being removed from wider recommendations.\n\nEngadget reported that LinkedIn's engineers worked with its in-house editorial team to identify engagement patterns associated with content that adds perspective, context, or expertise, rather than merely repeating existing ideas. The company had not publicly detailed a technical definition or detection methodology for AI slop, according to the report.\n\n### Detection remains a difficult moderation problem\n\nUser reporting adds a human feedback channel to automated feed-quality systems, but it does not establish whether a specific post was AI-generated. In comparable content-moderation systems, classifiers and user reports are generally used to rank or review content rather than provide reliable proof of authorship. For practitioners, stylistic markers often associated with generated text, such as formulaic phrasing or excessive formatting, can also occur in human-written posts.\n\n404 Media cited an estimate from AI-detection service Pangram that 41% of long-form LinkedIn posts and 30% of short-form posts were likely AI-generated. The outlet disclosed that Pangram had previously advertised with it. LinkedIn also offers generative AI writing features, making the reported moderation effort focused on low-value or repetitive content rather than AI assistance alone.\n\n## Key Points\n\n- 1LinkedIn added a user-reporting signal for suspected AI slop, expanding its tools for improving feed quality and recommendations.\n- 2404 Media reports the feedback can help tune LinkedIn classifiers, linking crowd reports with automated content-ranking systems.\n- 3Comparable moderation systems face false-positive risks because writing style indicators cannot reliably prove whether content was generated by AI.\n\n## Scoring Rationale\n\nThe feature is relevant to practitioners building content-ranking, trust, and moderation systems because it combines user feedback with classifier development. Its direct technical details remain limited, and it is a platform-specific feed-quality update rather than a new model or broadly available developer tool.\n\n## Sources\n\nPrimary source and supporting public references used for this report.\n\nPractice with real Social Media data\n\n90 SQL & Python problems · 15 industry datasets\n\n250 free problems · No credit card\n\n[See all Social Media problems](/problems/datasets/social)", "url": "https://wpnews.pro/news/linkedin-adds-ai-slop-reporting-option", "canonical_source": "https://letsdatascience.com/news/linkedin-adds-ai-slop-reporting-option-4e29c061", "published_at": "2026-07-30 15:15:30+00:00", "updated_at": "2026-07-30 18:28:29.692699+00:00", "lang": "en", "topics": ["ai-policy", "ai-products", "ai-tools", "artificial-intelligence"], "entities": ["LinkedIn", "404 Media", "Engadget", "Hari Srinivasan", "Pangram"], "alternates": {"html": "https://wpnews.pro/news/linkedin-adds-ai-slop-reporting-option", "markdown": "https://wpnews.pro/news/linkedin-adds-ai-slop-reporting-option.md", "text": "https://wpnews.pro/news/linkedin-adds-ai-slop-reporting-option.txt", "jsonld": "https://wpnews.pro/news/linkedin-adds-ai-slop-reporting-option.jsonld"}}