The internet has played a remarkable role in fostering human creativity. Through the internet, the whole world was given opportunities to easily share their craft, skills, interests, and knowledge with others. Your content didn’t need to wait several years to reach its deserved audience. It could now happen potentially overnight.

However, with the absolute barrage of content uploaded on the internet every day, it has also become tough to go through or find the best pieces. Gartner suggests that for such a purpose, artificial intelligence remains our best means currently. It mentions how more enterprises are going to use AI-backed systems for better autonomous metadata tagging their content. Such a finding is in line with the promise AI has shown in analyzing content better than any previous strategies.

This article will dive into how artificial intelligence and machine learning algorithms have come to understand and analyze content. It will explore concepts like content granularity and modularity and how AI/ML systems use them as they grapple with content.

Understanding Content Granularity And Its Importance

Before observing the performance of AI/ML systems at analyzing content, it helps to understand what content granularity means. The content needs of an individual can obviously vary drastically from that of an enterprise. Furthermore, how the content uses its medium also comes into play. A content management strategy keeps all such factors in mind when devising a game plan and defines the content granularity accordingly.

Simply put, content granularity means how much content you fit within a particular individual piece. A piece of content could be either fine-grained or coarse-grained, referring to whether it has the content spread over smaller or larger subcomponents, respectively. For example, a 2500 word blog post followed by 280 character follow ups would be coarsely-grained, and a thread of Tweets utilizing the 280 character limit is finely-grained content. 

The granularity of content can have a significant impact on its engagement with the audience. While finely-grained content is much easier to digest at a time, the total size of individual pieces can get too much to handle. However, coarsely-grained content can get difficult to follow and even be off-putting for some. 

Content granularity is also essential to the discussion of content modularity. How much content is within a specific piece determines whether you can use it in multiple ways/places or not. For a work to be modular, its subcomponents will have to be separable yet must also fit together as a whole. In general, the more granular content is, the more modular it is, and therefore the more reusable.

How AI/ML Can Help With Analyzing Content 

AI/ML algorithms are behind almost all of the cutting-edge technologies we currently have or are developing. Their power comes from their ability to learn from data, uncover hidden patterns, and understand how factors relate to each other. One of the most exciting implementations of AI/ML algorithms is the field of natural language processing. It is the area that comes into play when discussing content analysis on any level, granular or whole. 

To understand how the powerful capabilities of AI/ML systems are utilized for analyzing different types of content, let’s take a look at its most popular use cases.

BERT: The Algorithm That Enables The Google Search Engine’s Queries 

As the most visited website and the biggest search engine globally, Google has a lot of impact and relevance in this regard. Its efficiency at procuring stunningly accurate results to even the most poorly written queries drives its popularity. Its approach lies in how it indexes the internet and then uses cutting-edge NLP algorithms to analyze them and decide their relevance to a particular query. 

BERT is Google’s most prominent tool in its NLP toolbox. Based on a neural network-based technique, BERT understands the intent behind queries by analyzing each word within the full context and not just independently. Furthermore, similar NLP algorithms “crawl” through web pages to understand their quality and relevance. The whole practice of SEO is dedicated to satisfying these algorithms and ensuring top positions in the results.  

How Social Media Companies Use AI/ML For Content Analysis

Much of the daily content uploaded on the internet is through social media platforms. Photos, videos, GIFs, and posts are some ways users interact and share content with others. As the user tally grows on each platform, so will the content and the difficulty of keeping all of it in check for compliance to policies. Social media giants like Facebook, Twitter, and Instagram utilize robust AI/ML techniques like NLP and computer vision for such a purpose.

Facebook uses its deep learning-based DeepText algorithm to analyze textual content and further understand people’s interests, preferences, and any rules being broken. It also offers a joint understanding of textual and visual content. Twitter and Instagram use advanced computer vision approaches to blur potentially triggering material and ensure fair use within the platforms.

The above examples are some of the most comprehensive practical uses of AI/ML algorithms for content analysis. Such uses wouldn’t be possible without the capability of AI/ML algorithms to look at content granularity. Only then can these algorithms confidently process the content and conclusively produce solid analyses.

Using AI/ML To Achieve The Right Content Granularity 

Choosing the right level of content granularity can be tricky. For example, merging multiple short stories into a compilation preserves the granularity at the short-story scale. Sentence-level granularity doesn’t come into question in such a case. However, if we talk about a product’s description, sentence-level granularity will be helpful and ensure modularity and reuse as well. Regardless, content granularity depends heavily on the content strategy and the aim in mind. 

AI/ML algorithms can be beneficial for the tricky requirement of assessing a piece’s content granularity. Advanced NLP algorithms can determine a word’s place within the sentence as a whole and independently too (Google’s BERT). You can also use algorithms to view textual and visual content in conjunction (Facebook’s DeepText).

Such AI/ML algorithms that can assess the content quality and granularity can make systems that guide your content towards the desired granularity. The granularity can be checked to see whether the specific piece is too detail-intensive for the medium or appropriately structured. To make such systems more effective, you can use the rules of thumb for deciding content granularity, like fine-granular for more reusability and coarser granularity for creative content.  

An AI/ML-based content granularity assessment approach would be beneficial in a ton of different ways. Some of the ways are as follows:

• Help you see what your content is lacking 
• What granularity your content should have for desired audience engagement
• Your audience’s traits based on the content granularity they prefer
• Level of personalization you can inject in your content while maintaining its granularity

Making Better (And More Granular) Content With AI/ML

Our relationship with the internet and content has grown increasingly complex over the last two decades. Every day, we constantly make choices to create, consume, share, and evaluate content on the internet. To become better at content, we will have to invariably look at the place where we have most frequently looked for solutions to complex questions: artificial intelligence.

AI/ML algorithms have shown great promise in almost all of their applications. Assessing content and its granularity is no different, and they can show us unprecedented insights into content strategies.