AI Fiction in the Wild

To classify the fiction in WildChat, we used an LLM. We showed the model — specifically, GPT-o4-mini — the first three exchanges in each human-ChatGPT conversation and asked whether the user’s prompts contain a request for fiction (as well as fanfiction and/or explicit material). We provide a detailed explanation, specifying that fiction can be any “content that is imaginative, speculative, or not grounded in real-world facts,” including “original stories, speculative scenarios, or alternate histories.” We also provided one example input and a desired output—an approach known as one-shot prompting or in-context learning—using a real WildChat user prompt and the “correct’’ answer according to our schema. All three authors indepenently labeled 300 randomly sampled conversations, and we compared our consensus classifications to the model’s, finding high agreement. You can learn more in our paper.

The original research team recorded a hashed IP address for every conversation, so this site organizes conversations by IP address.

A hashed IP is not the same thing as a person. If someone used ChatGPT at home, at a coffee shop, and at the library, that shows up here as three separate IP addresses, so the IP addresses on this site are likely an overcount of the people behind them.

An earlier version of this site attempted to correct the overcount by bundling IP addresses into “estimated users.” We retired this feature for privacy reasons.

• Geographic region is shown at the country level only.
• Conversations flagged as toxic by the original WildChat authors are excluded entirely.

We determine clusters of story permutations by using the same clustering algorithm, DBSCAN. Each first prompt is encoded with the all-MiniLM-L6-v2 sentence transformer, and we cluster the resulting vectors with DBSCAN. Clusters are then shown per IP address, so a story permutation panel only ever covers conversations from a single hashed IP.

We do not include any conversations labelled “toxic” by the original WildChat authors. We do include conversations that were labelled sexually explicit by our own classifier. You can hide this content by toggling explicit “off.” Please note that not all toxic, offensive, or sexually graphic conversations are caught by these filters. This content can be upsetting and offensive.

The users featured in the WildChat data (Zhao, et al.) consented to have their data used for research purposes. You can read their paper for more details. However, ethical and privacy considerations remain. We might wonder how many of these users actually read the agreement or fully understood the implications of their decision.

Additionally, though the conversations are anonymized, and though users agreed to share their chats publicly, some users still shared personal and sensitive information. The WildChat authors implemented automated procedures to anonymize personally identifiable information (e.g., personal names, email addresses), but subsequent studies showed that sensitive data remained (Antoniak et al.). These authors notified the WildChat creators, who took steps to scrub this additional information.

In choosing to study and release this data, we acknowledge a trade-off of concerns. While we believe the potential risks and harms for the WildChat users have been sufficiently mitigated, some may still exist. We believe this analysis is justified by users’ explicit consent and by the urgency of helping researchers and the public understand what people are really doing with these tools.

Because the WildChat users are anonymous, we also do not have a clear sense of their demographic breakdown or representativeness. The chatbots were hosted on Hugging Face — a community for sharing machine learning models and datasets — so we might guess, as the original creators do, that these individuals skew more technically literate and “online” than your average user. These instances of ChatGPT were also free, did not require a login or account, and did not have the same rate limits as a regular account. This might mean that users are from lower-income backgrounds, from countries where ChatGPT is banned, or that they were more invested in pushing the boundaries of the model with explicit or prohibited content. WildChat is not a representative sample of all ChatGPT users, with recent research showing a particular dominance of power users compared to a random sample of Microsoft Bing Copilot user chat logs (Hicke and Tomlinson).