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When it comes to showcasing Contextual Ads on online content, publishers and seller-side platforms have traditionally relied on the IAB Content Taxonomy. However, when applied to news and editorial content, the IAB Content Taxonomy often falls short in depth, making it difficult to categorize content effectively. This limitation not only impacts advertisers, ad networks, and demand-side platforms by reducing their ability to deliver high-performing, brand-safe ads, but it also hampers publishers from fully monetizing their content.
In this article, We explore three leading categorization systems — IPTC Media Topics, IAB Content Taxonomy, and Google Cloud NLP Categories and discover how these frameworks can enhance the classification of news and media content, helping publishers maximize ad revenue while ensuring brand-safe contextual advertising.
IPTC Media Topics
The IPTC Media Topics taxonomy is a well-established subject classification system specifically designed for the news and media industry. Developed and maintained by the International Press Telecommunications Council (IPTC), this taxonomy:
- Focuses on news-related content: It’s tailored to meet the needs of newsrooms, publishers, and media platforms.
- Comprehensive and hierarchical: With over 1000 terms organized in a clear hierarchy, IPTC Media Topics provide a granular yet structured way to classify topics. You can find the list of topic on IPTC website .
- Standardized and widely adopted: As an open standard, it’s used by major news organizations around the world, ensuring consistency and interoperability.
The top-level IPTC Media Topics categories and the number of topics by top-level categories are shown below.
This taxonomy excels in domains requiring detailed subject tagging for editorial content.
IAB Content Taxonomy
The IAB Content Taxonomy is a categorization standard developed by the Interactive Advertising Bureau (IAB) to help advertisers, publishers, and technology platforms classify and monetize digital content. Key characteristics include:
- Advertising-centric focus: Designed primarily for targeting ads based on content relevance.
- Broad application: It’s widely used in programmatic advertising to match ad campaigns with appropriate content.
- Granularity for ad performance: The taxonomy includes specific categories that help advertisers refine their audience targeting.
The top-level categories and the number of topics under each of them for IAB Content Taxonomy 3.1 are shown below.
The IAB Content Taxonomy’s primary purpose is to optimize for Ad Placements. It lacks the depth and specificity needed for editorial content.
It can be understood by the example of the categories published in IAB content category 3.1 for some of the common news / media topics.
For example, The top-level category ‘crime, law and justice’ has 67 IPTC media topics whereas just one category ‘crime’ in IAB Content Taxonomy.
‘Crime, law & Justice’ category in IPTC Media Topics
Similarly, ‘Automotive’ — an important category with 47 subcategories in IAB Content Taxonomy, Has just 2 categories related to it IPTC Media Topic.
Google Cloud NLP Categories
Google’s Cloud Natural Language Processing (NLP) service includes a feature for text classification that uses a predefined set of content categories. These categories:
- Focus on machine learning: Google Cloud NLP automatically assigns categories to text based on its content.
- Predefined and fixed: The list of categories is static, limiting flexibility for niche or highly specific use cases.
- Designed for automation: It’s best suited for applications where quick, automated categorization is needed.
The top-level categories and number of topics under each of them for Google Cloud NLP Categories are shown below.
By looking at the category tree and number of categories, It is clear that Google provides a broader set of categories compared to IAB Content Taxonomy on similar lines, However, it’s limited to classifying content and not generic news categories.
Key Differences
Mapping between IAB Content Taxonomy, IPTC Media Topics & Google Cloud NLP Categories
Now that we’ve explored these three taxonomies in detail, let’s dive into how they can work together to deliver maximum value for both advertisers and publishers. One effective approach to leveraging these classifications for optimizing ad revenue while ensuring highly contextual and brand-safe ads is by creating a seamless mapping between the three categories.
For instance, a media or news website can tag each content page using the IPTC Media Code. This tagged content can then be analyzed using Google Cloud NLP’s classify text method to determine its corresponding Google Cloud NLP category.
By mapping these two classifications to the IAB Content Taxonomy, you can pinpoint the most relevant and brand-safe keywords or ads for your content. This strategy creates a powerful synergy that enhances ad performance while maintaining brand integrity.
Conclusion
Each taxonomy offers unique advantages, but for news and media websites, IPTC Media Topics stands out for its precise contextual depth and editorial focus. By integrating IPTC Media Topics with the IAB Content Taxonomy and Google Cloud NLP, publishers can uncover new opportunities to enhance ad targeting and boost revenue. This mapping not only supports editorial objectives but also aligns content classification with business goals, enabling more effective pairing of media topics with relevant and impactful advertisements.