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dataAvailable | States whether the classification request has already been processed. If processed data exists, Verity returns the results from the database. If not Verity starts a new processing request. |
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status | The current processing status of the analysis request. |
pageUrl | The URL of the page, video, image, or text analyzed by Verity, as applicable. |
uuid | A unique identifier generated for the classification request. |
languageCode | The standard ISO 639-1 code for the language of the content. Verity currently supports content in:
Refer to the Language Support Grid for the latest supported languages. Note: If Verity detects an unsupported language, a status of NOT_SUPPORTED is returned. |
iab | IAB contextual categories are defined in the IAB Content Taxonomy and are widely adopted in programmatic and Real-Time-Bidding (RTB) ad marketplaces. Verity supports current versions of the IAB Content Taxonomy. The Verity team keeps track of new taxonomy releases and implements updates in a timely fashion. Refer to the Verity Taxonomy document for a listing of IAB contextual categories. |
keywords | The top Keywords identified for the content, listed in order of prominence. |
safe | The final aggregated Brand Safety summary result for the content. If any threat classifications are identified with a risk level of VERY_HIGH, the safe value is false and the content is considered unsafe. If no (or low-risk) threat classifications are identified, the safe value is true, and the content is considered safe. |
threats | Threat categories are part of GumGum’s brand safety taxonomy. GumGum classifies content into nine threat categories. For a complete list of Threat category IDs and Names, refer to Threat Categories in the Verity Taxonomy document. To detect possible threats, Verity analyzes and scores all the extracted content. Verity then correlates the scores to determine a per-category threat risk-level for the content. Possible threat category risk-levels are:
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sentiments | Identifies and extracts opinions within digital content. The positive, neutral, and negative levels of sentiment expressed in the content are evaluated. For contextual targeting purposes, a sentiment level of neutral or positive is generally recommended. |
processedAt | The date and time of the classification. |
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Brand safety is a threat detection algorithm, so in this case Verity favors Recall over Precision. Data Scientists use Precision-Recall curves to maximize Recall with minimum loss in Precision, thereby maximizing the number of potential threats classified.
Verity results comprise risk and confidence levels for each Threat category.
The confidence risk level represents the risk potential of unsafe content within a page, video, image, or text string. Possible risk levels are LOW, MEDIUM and HIGH.
In traditional statistical measures, confidence in observed results may be assessed according to the number of samples involved in a test. Larger scale sampling leads to a higher confidence score. However, Verity confidence levels are not related to the quantity of sample data. The goal of Verity threat levels is to determine whether it is safe to display ads on a given page or video. For example:
A threat category result “confidence”: “VERY_LOW” should be interpreted as Verity identifying a very low risk for that category within the content, with a high level of confidence.
A threat category result “confidence”: “VERY_HIGH” should be interpreted as Verity identifying a very high level risk for that category within the content, with a high level of confidence.
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