The Content Safety Moderator API can detect various types of harmful content including toxic, offensive, hateful, threatening, and inappropriate text.

The API classifies content into several categories such as hate speech, harassment, self-harm, violence, and sexual content, providing a clear understanding of the nature of the detected harmful content.

The API returns confidence scores for each category of harmful content detected, along with an overall safety verdict indicating whether the content is safe or unsafe.

The API is designed for use in social platforms, forums, chat applications, comment systems, and any application that requires moderation of user-generated content to ensure a safe environment.

The API accepts text input for analysis and returns structured output in a format that includes category classifications, confidence scores, and an overall safety verdict.

The Moderate Content endpoint returns structured data that includes an overall safety verdict, risk scores, category classifications, and confidence levels for various types of harmful content detected in the analyzed text.

Key fields in the response include "safe" (boolean), "riskScore" (numeric), "riskLevel" (string), "categories" (object with detected content types), "flaggedCategories" (array), and "summary" (string describing the content's safety).

The response data is organized into a JSON structure, with top-level fields for safety verdicts and risk assessments, followed by a detailed breakdown of category classifications and their respective confidence scores.

The Moderate Content endpoint primarily accepts a single parameter: the text content to be analyzed. Users can customize their requests by providing different text inputs for moderation.

Users can utilize the returned data by interpreting the "safe" field to determine content safety, using "riskScore" for severity assessment, and referring to "categories" for specific harmful content types to inform moderation actions.

Typical use cases include moderating user comments on social media, filtering chat messages in forums, and ensuring safe content in online communities by automatically flagging harmful text for review.

Data accuracy is maintained through continuous training of the AI model on diverse datasets, regular updates to improve detection capabilities, and validation against real-world examples of harmful content.

Standard data patterns include a clear indication of whether content is safe or unsafe, detailed confidence scores for each category, and a summary that succinctly describes the analysis outcome, aiding in quick decision-making.

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