ShareChat: A Dataset of Chatbot Conversations in the Wild
Paper • 2512.17843 • Published • 2
Datasets:
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This dataset holds real conversations that people published from ChatGPT, Claude, Gemini, Grok and other assistants, collected from the open web. It contains personal information people chose to share and material that is offensive, explicit or harmful. Access is approved by hand, for research. By requesting access you agree to: use the dataset under CC BY-NC 4.0 (non-commercial, attribution), the terms the embedded ShareChat rows carry; make no attempt to identify the people in these conversations or to infer sensitive personal data about them; apply your own filtering and safety measures before training or evaluating models on it; respect the terms of service of the platforms the conversations came from; not redistribute the dataset; and delete any conversation the maintainers notify you has been removed.
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sharetrawl
246,150 publicly-shared LLM conversations across 9 platforms including toxic output.
Last updated: 2026-09-20
This dataset extends ShareChat (paper) and is updated weekly. 75,223 of its 246,150 urls came from ShareChat's list (31%) rather than from our own discovery.
Have share links this corpus is missing? Paste them at https://sharetrawl-submit.hails.workers.dev
Motivation
Public chat datasets lag live traffic by 6 to 12 months (WildChat-4.8M → July 2025; ShareChat → October 2025); and often redact harmful/toxic output (e.g. WildChat-Full). This limits research into in-the-wild safety harms and makes benchmarking monitors on real-world use-cases challenging.
Composition
| platform | conversations |
|---|---|
| chatgpt | 114,834 |
| grok | 80,589 |
| gemini | 19,288 |
| perplexity | 17,744 |
| claude | 8,109 |
| manus | 2,150 |
| deepseek | 1,278 |
| qwen | 1,169 |
| kimi | 989 |
| total | 246,150 |
Time distribution
The earlier of created_at (when the conversation happened) and captured_at (when we fetched
it).
| year | conversations |
|---|---|
| 2022 | 8 |
| 2023 | 16,604 |
| 2024 | 27,782 |
| 2025 | 85,643 |
| 2026 | 106,392 (to 2026-09) |
236,429 of 246,150 conversations carry one of the two dates; the remaining 9,721 appear in no year above, having neither.
Harm labels
labels/sharetrawl_broad_labels.parquet scores 245,355 conversations with a two-stage cascade: a cheap gate
(z-ai/glm-5.3-flash) admits anything scoring ≥ 5, and a stronger oracle
(claude-opus-4-8) gives each admitted conversation a 0–100 score and a list of categories. Both
scores are stored (gate_score, opus_score); flagged is oracle score ≥ 50, and
15,074 (6.14%) are flagged.
Evidence window: whole-conversation (labelled 2026-09-18, declared in
labels/METADATA.json): both stages read the whole conversation, in windows of
40,000 characters with the strongest verdict kept (n_windows, windowed).
52 rows (51 of them flagged) have no url at all.
The gate's misses are measured: on 2026-09-08, 2,000 gate-rejected conversations were scored by the oracle and 1 of 2,000 came back flagged — a false-negative rate of 0.05% (95% CI 0.01–0.28%), or roughly 76 missed flags (at most 432) across the 152,681 rejected. The true rate over the labelled subset is therefore likely near 6.17% (at most 6.32%), not 6.14%.
By category
A conversation can carry several categories, so these sum to more than the flagged total. Categories with 10 conversations or fewer are omitted.
| category | conversations |
|---|---|
| misinformation | 12,592 |
| deception | 6,827 |
| defamation | 4,067 |
| hate_harassment | 2,428 |
| privacy | 2,279 |
| sexual | 1,933 |
| other | 1,793 |
| jailbreak | 1,178 |
| illicit | 1,106 |
| self_harm | 792 |
| violence | 509 |
| unknown | 497 |
| cyber | 487 |
| cbrn | 326 |
| harassment | 251 |
| harassment_hate | 20 |
By platform
| platform | flagged | scored | rate |
|---|---|---|---|
| grok | 8,346 | 80,448 | 10.37% |
| kimi | 91 | 989 | 9.20% |
| deepseek | 99 | 1,173 | 8.44% |
| claude | 578 | 8,028 | 7.20% |
| qwen | 57 | 1,156 | 4.93% |
| chatgpt | 4,732 | 114,575 | 4.13% |
| manus | 86 | 2,142 | 4.01% |
| gemini | 682 | 19,123 | 3.57% |
| perplexity | 403 | 17,721 | 2.27% |
By source slice
| slice | flagged | scored | rate |
|---|---|---|---|
| sharetrawl | 14,638 | 235,638 | 6.21% |
| sharechat | 436 | 9,717 | 4.49% |
The gap is primarily platform mix, not corpus: within platform, text-deduplicated, the two are broadly comparable.
To break the rate down by where a URL was found, group on provenance_source; it is set on
155,781 of 245,355 scored conversations, absent on the earliest-discovered
rows, and thin enough per backend that small denominators dominate.
Turn-level labels
The cascade above scores whole conversations. Separate classifier layers additionally score every text-carrying turn, which is the unit ShareChat and WildChat report on.
| layer | model | flagged when | flagged | scored | rate | coverage |
|---|---|---|---|---|---|---|
| Detoxify | unitary/toxic-bert |
toxicity >= 0.5 |
33,645 | 1,985,426 | 1.69% | 78.9% |
| Llama Guard 3 1B | meta-llama/Llama-Guard-3-1B |
p_unsafe >= 0.9 |
95,373 | 1,985,426 | 4.80% | 78.9% |
| OpenAI Moderation † | omni-moderation-latest |
flagged, the file's own boolean |
63,947 | 1,903,616 | 3.36% | 75.7% |
| Broad cascade (turn-level) | claude-haiku-4-5-20251001 → claude-opus-4-8 |
flagged, the file's own boolean |
73,318 | 2,483,343 | 2.95% | 98.7% |
† OpenAI Moderation is still being scored. It walks the corpus in file order, so its coverage concentrates in the platforms early in the scan and is not a random sample — group by platform before trusting its rate.
Data structure
One row per conversation (not per message, unlike ShareChat's CSV release), with the turns
nested in a messages list.
| column | meaning |
|---|---|
platform |
responder platform (chatgpt, grok, gemini, perplexity, claude, manus, deepseek, qwen, kimi) |
url |
the public share URL |
source |
whose copy of a conversation this row is — sharechat (base census) or sharetrawl (our own capture) — not which corpus first found it |
provenance_source |
which discovery backend found the URL (github, wayback, …) |
provenance_ref |
the exact item that surfaced it — an archive snapshot, a scan record, or the public post, repo file or forum thread where the link was shared |
model |
responder model, where the platform discloses it |
created_at / published_at |
conversation timestamps, where available |
captured_at |
when we captured it — set only on fresh rows |
topic / language |
classified topic and language — ShareChat's labels on base rows; language on our own captures is identified by Claude Sonnet |
summary_headline / summary / summary_outcome / summary_terms |
a model-written English index entry: a headline noun phrase, two to four sentences ending with how the conversation visibly ends, an outcome (answered, refused, unfinished, empty, unclear) judged from the last visible exchange, and three to six lowercase public search terms. Written by Claude Sonnet 5 (the backfill) and GLM-5.3-Flash on zero-data-retention endpoints (the increment); names of private people and the user's own projects are deliberately absent. Empty where no pass has read the conversation yet |
summary_window |
full when the model read the whole conversation, head_tail when a long one was cut to its opening and ending — the summary then says "After an unseen stretch," before the ending |
turns_count |
number of turns |
messages |
list of {message_index, role, plain_text, thinking, tool_calls, code, analysis, version}. thinking is the reasoning the platform exposes for an assistant turn, as plain text (chain-of-thought on DeepSeek, Qwen, Kimi and Grok-on-X; reasoning summaries on ChatGPT, Grok and Manus; ShareChat's base-row values flattened to their content), empty where the share carries none (Claude, Gemini, Perplexity). tool_calls is a JSON list of the tools that turn ran, {kind, name, input, output} — kind is one of code, search, browse, file, artifact, image, memory, mcp, plugin, other; name the platform's own tool name; searched and fetched pages are reduced to [{title, url}] locators while code output and error text are kept whole; null output where the platform recorded none; empty on Gemini and on every base row. code / analysis / version are ShareChat's, base rows only |
published_urls.sha256 lists the sha256 of every url any retained revision has carried since
the repository's history was last purged, one per line — a superset of this revision's rows, label
files included. It is how a publish knows whether a since-withheld conversation was ever public
(see Removal requests), and it lets you check whether a url has appeared in this dataset without
downloading the parquets.
How the fresh slice is collected
Share URLs are discovered by unioning many backends — web archives, passive-scan corpora (urlscan.io, AlienVault OTX), code search (GitHub, SourceGraph), social/forum search (Bluesky, Reddit, Hacker News, Lemmy) and WeChat public-article search (Sogou) — then de-duplicated against what is already stored. Each URL is rendered in a real browser (please reach out for the HAR files).
Safety and exclusions
Child-safety material is excluded via an automatic screen that runs before every publish and fails
closed — a classifier refusal quarantines rather than passes, and a capture whose screen call
did not complete is withheld until a later run clears it. Quarantined conversations are held in a
content-free denylist of hashed URLs (no text, no URLs) and never enter the corpus. In the label
file, rows categorised csae retain the score and category but have url and id blanked. The
screen reads each turn's plain_text; a turn's thinking and tool_calls publish as captured and
have not been read by any classifier.
These are real conversations from the open web. They contain personal information people chose to share, and material that is offensive, explicit, or harmful.
Removal requests
If you are in one of these conversations and want it removed, or you hold rights in one, open a
discussion on this dataset's Community tab with the share URL (the url value). A removed
conversation leaves the corpus at the next weekly publish, and that publish purges the
repository's history (squashes it to one commit), so the conversation does not persist in an
earlier revision. Otherwise revisions are kept. Removals are recorded as a content-free list of
hashed URLs, never as the URL itself.
Limitations
- Shared conversations are not representative of all usage. People share what is interesting, impressive, funny, or outrageous.
- Platform mix is uneven and shifts over time, driven by what is discoverable rather than by real platform share.
- Harm labels are model judgements, not human annotated nor the truth.
Citation
Please reach out to me for a citation, as the major work is not yet released.
Cite ShareChat as well:
@misc{yan2026sharechatdatasetchatbotconversations,
title = {ShareChat: A Dataset of Chatbot Conversations in the Wild},
author = {Yueru Yan and Tuc Nguyen and Bo Su and Melissa Lieffers and Thai Le},
year = {2026},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2512.17843},
}
License
CC BY-NC 4.0, inherited from ShareChat — this dataset embeds the ShareChat corpus, whose data structure and distribution are licensed CC BY-NC 4.0 under the ShareChat Dataset License Agreement, so the original terms apply to the whole of it. Non-commercial use only, attribution required. The licence covers the compilation and our labels; the conversations themselves were written by their participants and the responding models, and the originating platforms' terms of service continue to apply to that content.
ShareChat's community guidelines carry over, and access here is granted on the same terms: make no attempt to identify the people in these conversations or to infer sensitive personal data about them; apply filtering and safety measures before training or evaluating on this data; respect the terms of service of the originating platforms; comply with applicable law and your institution's ethical review; the views in the conversations are not those of the maintainers; and the dataset is provided as is, without warranty of any kind.
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