Provenance
Can an AI detector judge a social post?
Quick answer
Not reliably. AI text detectors estimate from statistical patterns, and a caption or short post gives them too little text to estimate from. Independent tests found detectors inaccurate even on longer essays, and biased against non-native English writers. For short posts, judge the account, the source and the claims, not a detector score.
On this page
Why length matters so much
Text detectors look for patterns: how predictable each word is to a language model, and how that predictability varies across sentences. Those patterns only become measurable over enough text. A 40-word caption, a reply or a headline gives a detector almost nothing to work with, so its score swings on a single word choice. Sadasivan and colleagues argued that as language models improve, even long texts become hard to separate reliably; short ones are harder still.
What independent testing found
Weber-Wulff and colleagues tested fourteen detection tools on human-written, machine-written, edited and translated texts and concluded that none was accurate or reliable enough to support accusations, with editing and paraphrasing reducing detection sharply. Liang and colleagues found that several detectors flagged a large share of essays by non-native English writers as machine-generated. Both studies used longer texts than a social post, which makes their warnings stronger, not weaker, for captions and replies.
What to rely on instead
| Question | Better evidence than a detector score |
|---|---|
| Is this account a real person? | History, consistency, replies, links to a known site |
| Is this image real? | Provenance data, earlier copies, other sources for the event |
| Is this claim true? | The original source the post cites, or its absence |
| Did this writer use a tool? | Their disclosure, and in a dispute their drafts and history |
For images there are better tools than for text: provenance records and reverse image search, covered in checking Content Credentials and spotting AI images in your feed. For text, the useful questions are about the source, not the style.
For publishers
If you run a site with comments or community posts, do not auto-moderate on a text detector's score. Moderate on behaviour, links and reports. And if your own writing is ever questioned, the guide to being accused of posting AI content sets out the evidence that settles it. The wider provenance section covers the signals you can add to your own posts.
Sources
- Weber-Wulff et al., Testing of Detection Tools for AI-Generated Text (2023)
- Sadasivan et al., Can AI-Generated Text be Reliably Detected? (2023)
- Liang et al. (2023), detectors of machine-written text are biased against non-native English writers
Questions
Can AI detectors tell if a tweet was written by AI?
Not reliably. Short posts give detectors too little text to measure, so scores are unstable and often wrong.
Are AI detectors accurate?
Independent tests found none of the tools studied accurate or reliable enough to support accusations, especially on edited or paraphrased text.
Why do detectors flag non-native English writers?
Simpler vocabulary and common constructions make text more predictable, which pushes statistical detectors towards a machine verdict.
How can I tell if a social account posts AI content?
Look at the account's history, sources and behaviour over time rather than scoring individual posts.
Hannah Voss, Editor. Checks every guide against a working WordPress install and the networks' current documentation. Last reviewed September 2026.
Related reading
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- AI-generated content in social feeds: what publishers should doHow AI-generated images and text change what happens when your posts are shared, and the practical steps publishers can take: disclosure, provenance, bylines.4 min read