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Date:
February 25, 2022

How Metadata Enhances Content Discovery

Media companies spend a lot of time and money studying and modeling consumer behaviors. It's big business and a critical component of today's media marketplace. Entire companies, platforms with specialized engineering teams, academic researchers, entrepreneurs, and the public attempt to find the Holy Grail of search algorithms that provide the best way to recommend titles, so you don't change the channel.

Algorithms are components of computer programs that analyze data to identify market and user trends, track inventories, improve network traffic efficiencies, and provide content recommendations to new or long-time system users. Artificial intelligence and machine learning are increasingly incorporated to augment and add insights into data analysis that would take years to build into the datasets these programs use. But when it comes to recommending what program to watch, one type of information is so important that without it, algorithms would fail miserably: metadata.

We've written about metadata before , so there's no need to revisit all of that here. In this post, we will focus on how metadata contributes to the effectiveness of search algorithms and why getting it right can lead to increased revenue.

Metadata Feeds Search Algorithms

Over 1.7 billion households are now searching for a movie or TV show to watch over the air, stream, buy or rent. That number is expected to increase to 1.8B by 2026. At some point, whoever controls the remote has to think about what to watch. If they're searching on a VOD or OTT platform, they're entering titles, names of actors, genre type, age-rating, words, or phrases that describe what they'd like to see. If the customer has been on the platform for a while, the service has kept track of what they've watched and searched their catalogs for titles with similar styles of content.

Think about it this way: if you're searching for a title to watch with young children, your search is likely going to include a "G" (or comparable) age rating. That's metadata. If you're looking for a romantic movie shot in Ireland, both the genre and the location are metadata. If your favorite musician is Andy Gibb, and you want to find which movies or TV shows he appeared in, guess what? You're going to find out using metadata.

That's the high-level stuff. Studios, distributors and platforms utilize metadata that consumers don't even think about when building their search engines and algorithms. Here are a few examples of those kinds of metadata:

Depending upon the platform, the number of metadata fields varies. Some platforms may request more descriptive details on character traits, such as whether the lead is "kind" or "obnoxious." Is the story originally written for the screen, or is it based on a novel or actual events? Does the film have a strong female lead, or is it a film about a group of friends? These data add context to the film record and enhances search, classification and matching capabilities.

Additional subscriber and profile details are drawn upon to further define possible interests. For example, is the subscriber male or female? What is their income level? Where do they live? Are there children or senior citizens in the home? Which movies do critics or people near them recommend? These are key factors in personalization.

There can be thousands of bits of information used to suggest something for you to watch. For example, Netflix has 222M subscribers , each having dozens of data points about their content preferences and watch history. The amount of data processed for each search means not only must the programs and network systems themselves be extremely robust, the algorithms doing the work are very complex.

Figure 1 is a search algorithm. This one won the Netflix Prize, which the company crowdsourced to see if their search model could be improved by more than 10%. The winning team was awarded $1 Million. You can find details about their formula here .

Why Content Creators and Platforms Take Metadata Seriously

The theatrical, linear, streaming, online, or retail video content market is enormous. According to IMDb Pro, over 235,000 TV and movie titles are available in the US alone, and over 5.7 million available worldwide. The question for content creators and distributors is how will you stand out in such a crowded market? How will you get to the top of the search results? Can you even get noticed?

Notwithstanding whether the title is any good or not, metadata alone isn't going to get it near the top of the search results page, but it can help. Providing as much of it as the platform or store requests and developing it is a good investment of time and resources. Search algorithms do not care whether your title has data for each of the fields it utilizes, but you can be sure that if nothing is in the key fields, your title may be harder for consumers to find. The worst films still have metadata that describe them, especially if they've won awards for being bad. You may not have watched them, but most movie junkies have heard of " Plan 9 from Outer Space " or " The Room ." When you search for them, you're going to find them and see detailed information about the title, its plot, actors and director, and why people think it's so bad that it's worth watching.

Obtaining high-quality and effective metadata is not a task left to the uninitiated. Studios like Disney and platforms like Netflix and Amazon Prime Video have teams of employees or contractors whose job is to watch and annotate metadata for their original movie and TV titles. Companies who distribute titles via TVOD or retail stores know better metadata makes their titles more easily findable by consumers and thereby more marketable. That means more sales, more rentals, more views and more revenue.

Global listings and metadata is one of Spherex's core businesses and provides studios or platforms easy access to a massive data store of over 1 million unique titles, including artwork variations and trailers in 45 languages spanning over 140 locales. Covering many languages, versions, and formats, the Spherex datastore contains nearly 25 million title records for Hollywood's top movies and tv shows and titles produced worldwide. Title records are cleansed, normalized, localized and ready to use.

It's easy to dismiss or not be too concerned about a title's metadata quality because it's not something people see. But whether they realize it or not, it is something they use every day. With nearly 600,000 new titles released worldwide each year, competition for the top placement on results pages is only going to get more intense. Understanding the importance of metadata, taking advantage of its proper use can help get your content the audience attention it needs to positively impact your bottom line.

Related Insights

Automating Peace of Mind: Navigating YouTube's Global Guidelines with SpherexAI

For media companies distributing content across YouTube, compliance is no longer just a legal requirement—it’s a prerequisite for discoverability, monetization, and channel survival. YouTube enforces strict policies governing child safety, vulgarity, graphic content, and cultural sensitivity. For content owners, ensuring compliance across multiple categories and geographies is a complex and labor-intensive process. To address this issue, SpherexAI provides a scalable solution tailored for any content creator or owner.

YouTube’s Expanding Compliance Landscape

YouTube’s Community Guidelines cover a wide array of regulated categories. Content can be removed or age-restricted—and creators may face penalties—if videos violate policies on:

  • Nudity and sexual content: Content that includes sexually gratifying imagery or non-consensual sexualization is prohibited.
  • Violence and graphic imagery: Footage showing serious injury, bodily fluids, or torture intended to shock viewers can be flagged or removed.
  • Child safety: Content that exploits minors, includes inappropriate family content, or features children in dangerous stunts is not allowed.
  • Illegal or regulated goods: YouTube restricts promotion of firearms, narcotics, and gambling services, among others.

Managing compliance with each of these categories—especially when content is global and multilingual—is a logistical challenge for distributors.

Enter SpherexAI: Precision Compliance Automation at Scale

SpherexAI applies multimodal AI to analyze video content across dialogue, visuals, audio, and metadata. It detects compliance issues not only by scanning for policy violations but also by identifying subtle cultural or regional sensitivities that could result in content removal or limited distribution.

For example, the platform flags:

  • Dialogue with excessive profanity or sexual references, aligned with YouTube’s vulgar language policy.
  • Visuals showing partial nudity, firearm use, or dangerous stunts, which may trigger strikes or age restrictions.
  • Culturally sensitive depictions—such as religious imagery or portrayals of death—that may violate local norms and platform rules.

SpherexAI outputs include timestamped alerts and severity levels, allowing content owners to make targeted edits rather than performing full manual reviews.

Equal Rules for All Creators

Whether you’re a major studio releasing film clips or a digital-first creator uploading your first series, YouTube holds all content publishers to the same standards. Community Guidelines are enforced platform-wide, regardless of a channel’s size, history, or market familiarity.

This presents a significant challenge for new entrants. Many first-time creators or distributors may be unaware that a thumbnail featuring misleading imagery, a prank involving minors, or a scene with unedited drug references can lead to demonetization or a channel strike. But YouTube’s enforcement is uniform: content that violates policy is subject to the same sanctions across the board.

SpherexAI helps level the playing field by equipping every content team—regardless of experience—with access to the same tools used by top studios. Its patented knowledge graph, built on over a decade of regulatory insight and expert human annotation, powers its AI models with unmatched precision. The result: faster reviews, greater accuracy, and fewer costly mistakes.

Cross-Platform, Region-Aware, and Regulation-Ready

Unlike tools focused on metadata or age ratings alone, SpherexAI delivers:

  • Granular analysis: Scene-by-scene breakdowns for violence, vulgarity, sexual content, and self-harm risks.
  • Cultural intelligence: Predictive models assess content suitability across 240+ territories using Spherex’s proprietary “cultural distance” framework.
  • Workflow integration: The platform’s API allows integration into existing supply chains and CMS platforms for automated review at scale.

Reducing Risk, Unlocking Revenue

YouTube’s monetization eligibility hinges on content safety. Channels can be demonetized or de-prioritized in search and recommendation if flagged for repeated violations. Well-known creators Logan Paul, ScreenCulture, and LH Studios have all been sanctioned for violations. By proactively identifying and resolving compliance issues before publishing, SpherexAI empowers content owners to:

  • Avoid strikes or takedowns
  • Retain monetization rights
  • Accelerate time-to-market
  • Protect brand reputation

Conclusion

YouTube is a dynamic platform for global content distribution that requires rigorous adherence to evolving content standards. For studios, broadcasters, and new creators alike, SpherexAI offers an AI-powered safety net automating policy compliance while preserving creative integrity. When SpherexAI is integrated into your production workflow, you can publish confidently at scale, with full compliance, and with no brand risk.

Ready to streamline compliance and expand your YouTube strategy globally?

Book a demo or visit spherex.com to learn how SpherexAI can support your team.

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Spherex CEO Teresa Phillips Talks Practical AI for Global Content Localization at EnTech Fest

At this year’s DEG EnTech Fest, Spherex CEO and Co-Founder Teresa Phillips joined a panel to explore one of the most practical and impactful uses of AI in entertainment today: localization.

During the session titled “Practical AI For Speed and Savings in Localization,” Phillips shared how Spherex is leveraging AI to deliver “deep video understanding” that accelerates compliance and rating decisions in over 200 markets. As she explained, understanding the context—cultural, visual, and narrative—is crucial in determining whether a piece of content is suitable for audiences worldwide.

“AI can now detect not just what happens in a scene, but how it might be interpreted in different cultural and regulatory environments,” said Phillips. For example, in Scandinavian countries, if a trusted figure, such as a clergy member, commits an unethical act onscreen, it can dramatically impact a film’s age rating. SpherexAI is trained to identify these nuanced moments, flagging them for human review when needed.

Phillips also highlighted the role of AI in augmenting human decision-making, noting that “AI agents can be trained to ask humans the right questions—like whether the drinking in a scene is casual or excessive—ensuring more consistent, scalable evaluations.”

The conversation also acknowledged the broader industry shift that AI is bringing to localization workflows—from quality control (QC) to artwork generation, compliance, and project management. With automation poised to displace some entry-level roles, Phillips raised a key question for the future: “If junior roles are the first to be automated, how do we bring new talent into the industry? We have a responsibility in our organizations to create opportunities for the next generation.”

Joining Phillips on the panel were Silviu Epure (Blu Digital Group), Chris Carey (Iyuno), Kelly Summers (The Sherlock Company), and Duncan Wain (Zoo Digital), offering a 360° view on how AI is transforming the way stories cross borders.

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Why Content Differentiation Matters More Than Ever

In today’s fragmented global media landscape, a one-size-fits-all approach no longer works. Media companies face increasing pressure to tailor their content strategies to suit diverse regulatory standards, cultural norms, and viewer expectations.To thrive, they must adopt a new mindset—content differentiation—as both a business imperative and a competitive advantage.

What Is Content Differentiation?

Content differentiation is the strategic process of customizing how media is packaged, presented, and monetized based on the context in which it is distributed. Unlike basic content localization, which focuses mainly on language and format adjustments, content differentiation goes deeper. It aligns content with the regulatory, cultural, and commercial realities of each market, platform, and audience.

The goal is to ensure that content resonates locally while maintaining global scale. Differentiation helps media companies maximize reach, reduce regulatory risk, and improve monetization—all without compromising creative intent.

Why It’s Needed Now
  • Regulatory Complexity: Governments are tightening rules around age ratings, depictions of violence, sexuality, religion, and topics of national interest. These laws vary widely across regions, creating a compliance minefield for global distributors.
  • Cultural Expectations: What works in one market can trigger backlash in another. Cultural nuances—around gender roles, family dynamics, or social taboos—shape how content is perceived and whether it’s embraced or rejected. In many cases, outdated depictions of identity, relationships, or social dynamics can resurface as flashpoints when content is distributed years later in new markets.
  • The Importance of Metadata: Streaming platforms now host massive libraries with considerable overlap in titles across services. In this environment, having accurate, detailed metadata—including production details, talent, , and advanced descriptors—is critical for making content discoverable, marketable, and ultimately profitable. Without it, even high-quality content risks being overlooked.
Meeting the Challenge with SpherexAI

Solving these challenges requires more than manual review or basic tagging—it demands a scalable, intelligent system that understands both the content itself and its contextual significance. That’s where SpherexAI comes in.

SpherexAI is a high-fidelity metadata platform built to help media and entertainment companies implement content differentiation at scale. Using multimodal AI, it analyzes every frame of video—evaluating visuals, audio, dialogue, and on-screen text—to generate rich, actionable metadata that informs compliance decisions, discovery, and monetization.

SpherexAI extends beyond basic content tagging. It analyzes material against global regulatory requirements, identifies cultural nuances and sensitivities, and detects potential risks prior to distribution. Additionally, it enhances content visibility in crowded platform environments by enriching metadata with precise descriptors, scene-level details, emotional tone analysis, and contextual insights—elements that improve content discovery and ad targeting.

Learn More

If you're ready to differentiate your content for every audience, platform, and region, SpherexAI can help. Contact us to schedule a demo or speak with our team about how metadata-driven intelligence can power your global strategy.

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