How can you tell if a written post in Facebook is AI enhanced?

1. Why a label from Meta matters — and why it’s not a silver bullet

Meta has committed to applying an “AI info” label to posts when its systems detect industry-standard signals that content was generated or substantially edited by AI, and it is building classifiers to detect AI content even without invisible markers. This label increases transparency on Facebook and Instagram and can include user disclosures for AI-generated video or audio, with potential penalties for non-disclosure ^{[1] [2]}. However, Meta warns about scale and accuracy problems—high content volume produces false positives and false negatives—so a platform label is a strong signal but not a definitive forensic proof; it reflects detection thresholds and policy choices as much as technical certainty ^[1].

2. Textual fingerprints: what linguistic signs point toward AI authorship

AI-generated text commonly shows overly polished grammar, repeated phrasing, generic tone, and occasional factual lapses or nonsensical details, making it read “too perfect” or oddly formal compared with human posts that include idiosyncratic errors, personal anecdotes, or emotional texture ^{[1] [4]}. Detecting these patterns requires critical reading: look for abrupt tone shifts, repeated sentence structures, lack of verifiable personal detail, or an absence of localized context. These cues are helpful but heuristic—they raise suspicion rather than deliver proof—because skilled human editors and prompt engineers can remove many telltale signs and AI models continually improve ^{[4] [5]}.

3. Toolkits and third-party detectors: more evidence, more uncertainty

Independent detectors and checklists—ranging from academic tools to commercial products like Copyleaks and GPTZero—can score text on how likely it is AI-generated, aiding verification efforts. These detectors analyze statistical patterns, perplexity, and repetitiveness to produce probability estimates; they are useful triage tools for journalists and moderators but frequently return probabilities, not certainties, and can misclassify polished human writing as AI or miss thoroughly edited AI text ^{[3] [6]}. Relying on multiple detectors and human review reduces error but does not eliminate it; detection remains an evidentiary exercise, not binary adjudication ^[3].

4. Practical steps you can take on Facebook right now

Start with the platform: check for an AI label or disclosure attached to the post, then inspect writing for generic phrasing, perfect grammar, or inconsistent personal detail. Use reverse-search for copied passages, run the text through multiple AI-detection tools, and consider metadata or posting patterns (rapid cross-posting, timing, or identical copy across accounts) as contextual clues. Fact-check claims and seek the original source for quotes or images; when in doubt, treat the post as unverified and avoid amplifying it. These steps combine platform signals, linguistic reading, and tool-assisted checks into a practical verification workflow ^{[1] [5] [4]}.

5. The arms race: why detection and evasion are both improving

Detection tools and platform classifiers are improving, but AI content creators and toolmakers are simultaneously refining models and post-processing techniques to remove detectable markers and mimic human idiosyncrasies. Industry efforts to embed invisible provenance markers in model outputs are promising but inconsistent across vendors and media types—images have earlier adoption than audio and video—and automated classifiers still struggle when outputs are heavily edited ^{[2] [3]}. The result is an ongoing cat-and-mouse dynamic: detection raises the cost of undisclosed synthetic content but cannot eliminate it, making layered verification essential.

6. What the different sources agree on — and where they diverge

All reviewed sources agree on two points: AI-generated text often looks different in predictable ways (overly perfect grammar, repetition, lack of texture), and detection tools—including Meta’s labels and third-party detectors—are imperfect and probabilistic ^{[1] [4] [3]}. They diverge on emphasis: platform messaging highlights labeling and policy enforcement as scalable fixes ^[2], while independent commentators stress human-led verification and tool pluralism because of persistent false positives/negatives and evolving model capabilities ^{[5] [3]}. The practical takeaway is triangulation: no single method proves AI authorship; combine platform labels, linguistic scrutiny, and multiple detectors while noting each method’s limitations ^{[1] [5] [3]}.