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Twitter no longer wants to show you just what's new in its search results20p Archives it wants to show you who tweeted it best.

The platform has moved further away from reverse chronological order in search to relevance order. Now when you look for something using Twitter's search bar, you'll be shown relevance-ordered tweets from across Twitter first on the results page.

The change occurred in September, according to a Twitter spokesperson, but was announced publicly Monday.

If you still want to see the most recent live tweets, the "latest" filter is still there, as well as "news," "photos" and "videos."

While alterations to the Twitter timeline algorithm caused the internet to briefly lose its mind in early 2016, changes to search are unlikely to cause much of a stir.

However, both tweaks show the social media company's obsession with the difficult concept of "relevance."

Announced in February, Twitter's opt-in timeline changes prioritised what it assessed to be "the best tweets" in a user's timeline, rather than simply showing them the most recent tweets from the people they follow.

In search, the freshest tweets don't necessarily contain the most useful information, but relevancy isn't simple either. Especially when you have to cull from all of Twitter and not just from the accounts users follow.

In a blog post, Lisa Huang, senior software engineer on Twitter's search quality team, explained the difficulty of prioritising so-called relevant tweets in search results.

The team are using machine learning to help decide how tweets will be ordered. "A person's behaviour on Twitter provides an invaluable source of relevance information," she wrote.

"Using this information, we can train machine learning models that predict how likely a Tweet is to be engaged with (Retweets, likes and replies). We can then use these models as scoring functions for ranking by treating the probability of engagement as a surrogate for the relevance of Tweets."

It's not that simple. As she notes, the likelihood of someone engaging with a tweet is also heavily influenced by where it appears. In other words, you're much more likely to retweet something if it's the first thing you see.

The team had to engage with this bias, along with the "noise" of Twitter -- for example, accidentally "liking" a tweet -- when pulling out the most relevant search results for users.

Diversity of results also mattered. Showing tweets from five news outlets who are all sharing the same video is not so helpful -- but which outlet's video should get priority? Huang wrote that the team is still working on adding diversity to search results.

Safe to say, we'll see more of this in the future.