# Echo restatement reply Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: echo-restatement-reply - Category: Platform-specific (platform) - Subcategory: reply-shape - Also known as: comment that restates the post, agreement echo, bulk comment, reply-guy paraphrase, quote-post paraphrase plus lesson, restatement comment - Status: Active. Currently signals low-effort writing. - Severity: medium - Evidence grade: primary-doc (platform policy, vendor documentation, or a model card) - Languages: en - Added: 2026-08-14 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/echo-restatement-reply ## Description A reply that paraphrases the post it answers and stops. It opens with agreement, restates the argument in the parent's own vocabulary, then asks a question the parent already answered. LinkedIn lists this as one of three named enforcement targets. Metric: the share of the reply's content words that also occur in the parent post, after stopword removal and lemmatisation. The 0.5 threshold is an operating point rather than a measured cutoff, because no published corpus supplies one; the reasoning is that a reply which shares more than half its content words with the post has at minimum restated it, while a reply that quotes one phrase and then argues keeps its own vocabulary and lands lower. Score only replies of 25 content words or more, and pass any reply that introduces a proper noun, a figure or a contradiction regardless of overlap. Calibrate on your own reply corpus before enforcing. A reply that carries nothing from the parent at all belongs to generic-affirmation-comment; this entry is for the reply that hands the parent its own words back. ## Why it reads as machine-written Comment automation has the post text as its only input, so overlap with the parent is high by construction. A person who read the post and had a reaction brings vocabulary the post did not contain. The signal is the overlap plus the absence of anything new, and either half alone is worthless. ## Detection Type: statistical (a measured metric against a threshold with a stated basis) Metric: parent-post-content-word-overlap-ratio Threshold: 0.5 (fires when above) Threshold basis: > No published measurement of reply-to-parent overlap exists for either human or model replies. Half the content words shared with the parent post is a FeedSquad review trigger set against our own reply corpus, chosen because a reply above it is restating the parent rather than adding to it. The overlap ratio is the observation; the emptiness after the restatement is the actual tell. ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Absolutely agree. Consistency really is the foundation of a good content strategy, and posting regularly does build trust with your audience over time. Trust is what turns followers into customers. What cadence would you recommend for a smaller team? After, repaired: > This held for us until it stopped. We posted three times a week for a year, then cut to one long piece a month, and inbound stayed flat while the writing time dropped by about two thirds. Cadence bought us nothing after the first quarter. What changed things was having something specific to say. Note: The repair contradicts the parent using the writer's own evidence. Agreement is not the problem. Agreement in the parent's words with nothing added is. ### Specimen 2 Before, exhibiting the tell: > Great points here! Onboarding really is critical for reducing churn, and investing in the early user experience does pay off across the customer lifecycle. Thanks for sharing these insights. After, repaired: > The early-experience point matches ours, but the fix was smaller than expected. Cutting our signup form from nine fields to three moved first-month churn from 31 to 19 percent. Everything we did later inside the product moved it less than that one form did. Note: Overlap with the parent drops because the reply is carrying its own facts. Figures in this repair are invented for the specimen. ## False positives Who legitimately writes this way. Support agents and community moderators restate a question before answering it, because a reply that quotes the ask reads as attentive and survives being read out of thread context. Teachers do the same when answering in public. The tell is a restatement that adds nothing after it, not the restatement itself. ## Model attribution No family attribution. This is a property of comment automation rather than of any model, and no platform discloses which tools or models produce the replies it labels. ## Platform notes - linkedin: LinkedIn names responses that simply restate the original post without adding anything new, alongside comments created at scale by automation with minimal human involvement. Flagged content stays visible to connections and loses out-of-network distribution. LinkedIn reported 94 percent accuracy at identifying generic content in initial testing and published no false-positive rate, so treat that number as the company's claim. - x: X's published enforcement rules attach a RiskyHighVizReply label for 30 days when the llm_slop_post label is present, and the account-level llm_slop_user label attaches SpamHighRecall, which routes to a timeline drop rule. Credibility prechecks skip high-follower and high-PageRank accounts before slop enforcement is evaluated, so the pattern is enforced hardest against small accounts. The classifier prompts themselves are withheld from the public repo. ## Sources 1. Keeping conversations real on LinkedIn (Laura Lorenzetti, VP and Executive Editor, LinkedIn Global Editorial, 2026-05-20) https://www.linkedin.com/pulse/keeping-conversations-real-linkedin-laura-lorenzetti-9821e (tier: primary-doc; accessed 2026-08-14) 2. Hari Srinivasan, Chief Product Officer, LinkedIn: AI slop report control announcement (2026-07-30) https://www.linkedin.com/posts/hsrinivasan1_ai-slop-is-a-top-priority-for-all-of-us-share-7488612006321889282-Ps8Z/ (tier: primary-doc; accessed 2026-08-14) 3. xai-org/x-algorithm: enforcement_post.yaml, enforcement_user.yaml, grox/flows/reply_spam (llm_slop_post and llm_slop_user rules) https://github.com/xai-org/x-algorithm (tier: primary-doc; accessed 2026-08-14) 4. LinkedIn newsroom, authentic content and conversations (Mar 2026) https://news.linkedin.com/2026/authentic-content-and-conversations (tier: primary-doc; accessed 2026-08-14) 5. eric-sabe/slop-lint, social-reply-register molds https://github.com/eric-sabe/slop-lint (tier: community; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-14, Active: The pattern is named verbatim in LinkedIn's own policy and is enforced on X through the reply-spam path. The threshold is provisional and flagged as such in the description; it is the weakest part of this entry and should be recalibrated once a reply corpus exists. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.