With a whole bundle of data right at the end of a simple Twitter search I've always thought it would be an awesome idea to somehow make use of it for user research. Specifically vernacular and terminology research.
I previously had ambitions of building some kind of machine learning system to extrapolate all kinds of awesome metrics from that data. That project is semi-on-the-shelf for the moment but that doesn't mean I can't still somehow use the search data in a more high level way.
Take a particular blog post that was written some time ago but does not perform as well as you feel it could. Head to Twitter advanced search and enter a few key terms from the post to bring up tweets somehow related to your topic.
Read through the list, note some down into a list, refine the search, note down more. Be sure to get a lot – try make sure you have mostly directly related tweets to what your topic is but also include some loosely related items and a handful that are borderline.
Partial match data is still good at this point but do exclude any that are obviously entirely unrelated to your needs. In a machine learning environment unrelated items would be good test data but manually they'll just add clutter.
Once you have a nice big list of tweets somehow linked to your topic choice take another read through them. Pay attention to the connecting words and phrases in them people use to bind the topic and objects together. Those are the words you'll use in linking phrases for an article.
Sometimes its harder to spot commonality within these linking phrase because the words don't have as much force as the specific key phrases we are searching for. That's why it's important to pay attention to them as much as you can – they are hard to discern from data gathered from searching only key phrases.
The first thing to do is to find the questions people are asking about the subject matter. Are many people familiar with it? Do people have similar complaints? See the same question being asked again and again?
Finding questions can be done multiple ways. Checking for shares to sites you know people ask questions on is a good way. Searching for words that can indicate questions (‘Who’, ‘What’, ‘When’, ‘Where’, ‘Why’,’ Will’, ‘How’ and ‘?’).
Knowing what questions people ask is a good way to spot any sticking points at various levels of expertise in the subject.
A side benefit of searching for shares to question sites is that it may also lead you to a better description of that question. Sometimes even the answer to many of those questions are at the links.
Knowing both the questions people have and the answers to those questions can be a great place to start refining posts or any content ideas you may have.
Sometimes there can be affinities between various topics that are seemingly completely unrelated. In any given group the people who like one things might majoritarily like something else. I cannot think of any real-world examples that have been proven to be accurate however I can give a few examples.
Lets say in a group of 10 people there are 5 cat owners and 5 dog owners. 4 of the cat owners like smooth peanut butter. 2 of the dog lovers like it too. You could say there is a strange affinity between cat owners and a preference for smooth peanut butter.
Another take on the above example might be that since 6 out of a total 10 pet owners prefer smooth that might imply that pet owners have an affinity with smooth peanut butter.
That's only a single, made-up, scenario with 2 provided perspectives. There are so many unseen affinities within different groups of people and subject matters that being able to correctly identify the ones that fit your audience profile is a huge boost to how likely people are to identify with the content you create for them.
Also if my above example is true then it makes total sense to somehow include smooth peanut butter on all of your cat related content. Keep that in mind for the future 😉