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Operating as a fee-based third-party service in the cloud, Verity can be integrated by publishers into content management systems (CMS) or data management platforms (DMP) to analyze and optimize media content.
Supply-side and demand-side platforms (SSPs and DSPs) can implement the Verity service on their own technology platforms, ad exchanges, and ad servers. There There are two primary product use cases:
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Verity applies natural language processing (NLP) and computer vision (CV) based machine learning techniques to analyze digital content. Multiple kinds of content can be analyzed, such as desktop and mobile web pages, images, and Online Video Platform platforms (OLV) and Over-The-Top (OTT) videos (including audio).
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Video analysis leverages GumGum’s industry-leading NLP text analysis and CV image analysis processes, plus fast and accurate audio transcription services.
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Verity is the only solution that applies machine learning techniques to provide content-level brand safety and contextual analysis. Alternative solutions may only leverage keyword methodologies to look at that consider the text and are limited to page-level analysis, use of Allow or Block listsBlocklists, or URL-level analysis. These more crude cruder contextual approaches often eliminate safe and relevant inventory. They also miss relevant content (e.g., keywords that are spelled differently), overlook related content, and mistakenly target irrelevant content (e.g., keywords with multiple meanings).
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Customers use Verity to analyze specific digital content and determine the eligibility of the content for ads. Verity does not crawl the internet for content; instead, a client application calls Verity (via their integration with the Verity API) specifying the URLs of specific content they’d like to analyze.
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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. 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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