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Jake Goldman of 10up shared several ways on the Business Storytelling Podcast how WordPress allows content creators to use machine learning.
Tagging and classification of content
Traditionally content creators would add tags themselves and sometimes even type them in. That can lead to multiple versions of the same tag.
In a machine-learning model, the computer will read the content and send back relevant tags. It can also add it to categories.
This can be helpful with large teams, which can end up with several versions of the same tag as people type it in. Categorization can be non-standardized when each content creator picks their own categories. I have a few dozen categories on here and what should go into Workflow or Content Creation or both can be debatable.
A computer taking care of categorization of content can be very helpful here.
Automatic tagging is being used elsewhere for personalization, too. For example, when content gets distributed via Flipboard the service categorizes stories by tags which users follow.
It’s not always perfect. Sometimes a story is tagged “Cedar Rapids” which is where I live but the city wasn’t mentioned in the story.
Other times, it helps stories take off. My story on Bacardi and its ties to Puerto Rico got a ton of readership from the “Puerto Rico” tag. Flipboard correctly showed it there.
Do tags help with SEO?
I’ve seen tags show up in Google Alerts – like when somebody tagged my name in a post. With more general keywords it might not be as important as it once was, but can help with some.
I would predict that Google is using way more text analysis but it certainly wouldn’t hurt to provide tags. By text analysis I mean they will analyze the text to determine what it should rank for.
It’s similar to meta descriptions. When I reviewed some results I noticed that Google results uses fewer suggested meta descriptions and more of text analysis already. Google then picks what it thinks the meta shown to searchers should be.
This is where machines tag and identify what’s in an image and even add description of what’s in an image and alt text. Example: “Mountains.”
Alt text is often overlooked and WordPress now reminds creators to add it. Using a machine to add descriptions can help content be found and takes care of an often forgotten strategy.
This will likely get more and more sophisticated. Facebook already tags people based on their images alone. This may very well be something that all content creators can use at some point.
Automatic center-point cropping of images
This is a great tool when it actually works out well. Jake mentioned that images are needed in various formats – thumbnails, featured images, social media previews, etc. A machine can learn how to better center crop them, meaning it uses the center to adjusts the image for different needs.
All these different places and networks have different dimensions for images. I use Adobe Spark to resize at times. While it’s easy, it still takes time. Often that works but a smart machine that can help us crop images better would be nice.
The cropping for now isn’t working well on social media. I shared my event coverage page before on Facebook and this was the thumbnail that was pulled in:
This commenter is right that is not a good crop.
Changing it was harder than it sounds as WordPress holds on to images once uploaded and even when deleted. I finally got it to look better:
Of course content gatherers will have to keep enough margin space so to speak on images when they take them. That could make the cropping easier. The Adobe Summit image is much easier to crop because it has more space around the meat of the image.
These are a few more examples of how machine learning and technology can help us make content creation more efficient. As always, people wonder if advances can kill jobs. Sure it does, but… “did spellcheck kill jobs?” Jake asked. “Probably not. Maybe somebody has to do less editing now.”
It makes people’s lives easier and certainly we have to evolve with new technologies. Somebody still has to oversee the strategy and make sure the technology does what it’s supposed to do.
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