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AI-generated and AI-assisted writing appears frequently in several recent social-media samples, but no study establishes what share of every post across every platform is AI-made. The largest estimates are detector-based analyses of selected posts, not a census—and a detector’s classification is not proof that a person used AI.
What recent social-media samples found
The clearest platform-by-platform figures come from Originality.ai’s 2026 analysis of sampled long-form posts. Its detector classified a post as likely to contain AI-generated text when at least 15% of the text appeared AI-generated. That threshold does not mean the entire post was written by AI.
| Platform and sample | Posts classified as likely to contain AI text | What the figure represents |
|---|---|---|
| LinkedIn, first seven months of 2026 | 73.6% | Originality.ai’s sampled posts; the report does not provide a fully comparable sample specification for this time-window figure. |
| LinkedIn, July 2026 | 75.6% | Originality.ai’s sampled posts; at least 15% AI-generated text under its detector threshold. |
| Facebook, July 2026 | 65% (126 of 195) | Randomly selected, long-form public posts in English. |
| X, July 2026 | 63% (127 of 201) | English-language posts at least 100 words long. |
| Reddit, June 2026 | 29% (162 of 559) | Posts at least 100 words long; the reported sample was not specified as English-language in the summary. |
These percentages describe Originality.ai’s detector results for its samples, not the proportion of all content on those platforms. The Facebook, X, and Reddit samples differ in collection details and, for Reddit, month; the values should not be read as a controlled platform ranking. Originality.ai also reported that 67.6% of its sampled LinkedIn posts from July 2025 met its likely-AI classification, compared with 75.6% in July 2026. Its collection method changed in November 2024, a relevant caveat when interpreting longer-term trends. Originality.ai’s report and methodology
A second large dataset measured a different thing
Pangram collected opt-in browser-extension scan statistics for 1,002,627 posts across LinkedIn, Medium, Substack, X/Twitter, and Reddit beginning April 24, 2026. Its extension scanned posts longer than 50 words. Pangram reported that 25.72% of items over 250 words in its dataset were fully AI-generated according to its model. It also distinguished fully generated writing from mixed or AI-assisted text. These results are not directly comparable with Originality.ai’s threshold for posts likely to contain at least 15% AI-generated text.
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The Pangram data reflects posts people chose to scan, not a probability sample of everything users encountered. Within that dataset, LinkedIn accounted for 62% of flagged AI content while making up about a third of scanned items. That is a distribution within Pangram’s scans, not a claim that LinkedIn has the most AI content overall. Pangram’s analysis and sampling details
Why the percentages do not add up to one answer
Each study measures a different population. A random sample of public posts, posts found through a collection method, and posts scanned through an opt-in extension can yield different results even when examining the same platform. The studies also vary in language, post length, dates, and whether the detector counts possible AI assistance or only text classified as fully AI-generated.
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- Thresholds differ: “Likely to contain at least 15% AI-generated text” is a broader category than “fully AI-generated.”
- Length matters: Several estimates focus on long-form writing or impose a minimum word count; they do not describe short captions, images, or videos.
- Samples differ: Some studies randomly selected posts, while others analyzed public-search collections or opt-in scans. None represents every post users see.
- Coverage differs: The cited social-post analyses focus on text. Their estimates do not establish the prevalence of AI-generated images, audio, or video across social media.
For broader internet context—not as a social-media estimate—Pew Research Center analyzed 490,000 English-language webpages sampled from Common Crawl with Open Pangram. Ten percent of its random July 2026 page sample showed significant signs of AI authorship; among pages published after ChatGPT’s public release, the share was over one-third. Webpages are not social posts, so these figures should not be substituted for platform rates. Pew Research Center’s webpage analysis
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A detector identifies patterns that its model associates with AI-generated text. It cannot reliably establish who wrote an individual post or whether a person used an AI tool. Pew cautions that detection systems sometimes classify human-written documents as showing signs of AI authorship, and sometimes miss AI-authored documents. Its analysis concerns large-scale linguistic patterns; it is not a reliable verdict on an individual post.
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The evidence here does not establish an independent, cross-platform accuracy rate for detectors on social-media material across languages, formats, and post lengths. Treat vendor classifications as estimates of AI-like patterns, not confirmed authorship. Pew’s discussion of detection limitations
Do people feel that AI slop is taking over their feeds?
A February 5–9, 2026 online survey by Sprout Social and Glimpse asked 2,250 social-media users in the United States, United Kingdom, and Australia about their experiences. Fifty-six percent said they often or very often see “AI slop” in their feeds, and 88% said AI-generated video tools had eroded their trust in social-media news.
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“AI slop” is informal shorthand for low-quality AI-generated content, not a consistent technical category. These figures measure respondents’ perceptions in three countries; they do not count posts or prove that AI video caused a decline in trust. Sprout Social and Glimpse survey findings
AI content in misinformation samples is a separate measure
A MediaWell summary of an October 2025 European online-disinformation measurement reported AI-generated content in 24% of sampled TikTok misinformation posts and 19% of sampled YouTube misinformation posts. More than 83% of those AI-generated items carried no label, according to the summary.
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The denominator is misinformation posts in the study’s samples—not all TikTok or YouTube content. These figures therefore cannot answer how much AI content appears across either platform generally. MediaWell’s summary of the October 2025 measurement
What platforms say they do about AI-generated content
A 2026 CHI study examined AI-content governance across 40 popular social platforms. Its abstract says just over two-thirds explicitly described governance for such content and identifies six types of action:
- Applying existing moderation policies to AI-generated content.
- Labeling or requiring disclosure.
- Setting AI-specific restrictions.
- Limiting monetization.
- Adding safeguards to AI-generation tools built into a platform.
- Providing user resources or feed controls.
The presence of a stated policy does not show how consistently it is enforced or how well it works. The 2026 CHI study abstract
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Several studies find substantial AI-like text in selected social-media samples, but their different populations and definitions prevent a single reliable estimate for all feeds. The figures are useful signals of a visible trend—not a census of social media or proof about any individual post.
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