With precisely zero fanfare, Twitter changed the way it picks and shows search results.

Following Facebook

Emulating how Facebook handles its feed, Twitter search now shows the most 'relevant' posts first rather than the latest ones. 

Aware that not everyone will like the change, Twitter remains flexible. Users can still manually filter their search results according to ‘relevant tweets’ or ‘latest tweets’. The default setting, however, is now all about the relevance.

Complex relevance

Understanding what makes something relevant is pretty hard for a computer to do. It's something the engineers at Twitter have grappled with.

In blog post from late 2016, Lisa Huang, senior software engineer in Twitter’s search quality team, explains the difficulty of prioritising relevant tweets in a search result.

“They could be searching for popular Tweets to engage with or to better understand context around the search query, and the most recent Tweets are not necessarily best suited for that,” writes Huang.

Making it relevant

To ensure greater relevance, Twitter retrieved tweet candidates from various sources within a larger time range and ranked them with a machine-learned model and performed several experiments with variables.

The machine learning method helped the Twitter team to decide how tweets will be ordered.

“A person’s behaviour on Twitter provides an invaluable source of relevance information. This data contains both attributes about the Tweets shown and the consumer’s reactions to them. Using this information, we can train machine learning models that predict how likely a Tweet is to be engaged with (Retweets, likes and replies),” writes Huang.

Relevance engages 

Apparently, the Twitter team actually implemented the algorithm update in September of 2016, but opted to ‘test the impact’ for a couple of months before publicly announcing the change.

Since going live, Twitter has found that users who experienced this new Search results page engage more. Also, users presented with relevant results Tweet more and interact more with other users when compared to users presented with recency-based results.

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