This is a temporary Page until Eleanore updates this post
Originally launched this way, the change comes just a few years after Twitter created an algorithm that identifies and provides the user’s ‘most relevant’ tweets first. The change created quite an uproar when it first happened and has been the bane of many users since. Many feel that the chronology is what gave Twitter its benefit and have been demanding this reversion. This Verge.com article from 2016 quotes a user talking about the analytic-based feed:
“It tears conversations apart, and it’s really confusing when some people have been live-tweeting an event and those things get scattered all across my timeline. It makes it extremely hard to follow events, and destroys one of the core values of Twitter, in my opinion.”
It’s difficult to argue with this. I’ve definitely felt the challenge of following a breaking news story in real time since the change has been made.
But Twitter created the relevance algorithm for a reason. They have a major problem that they need to address. Basically, if you follow a decent number of users, how can you expect to find anything relevant among all the content that’s posted? It’s a signal to noise ratio problem. There is some amount of signal buried in a lot of noise. I have been bothered by this problem for some time, even though I only follow a bit more than 300 people (at the time of writing this). It has made me very judicious about who I follow.
This leads to a data triage challenge and it’s not unique to Twitter. Almost every popular technology company has this issue. Amazon has millions of products for sale. How am I going to browse and find something to purchase? Netflix has thousands of TV programs and movies available. How can I find something interesting to watch? Facebook and Instagram feeds are similarly clogged with posts. The more data that is collected for a system, the more difficult it is to find the relevant information in that data.
When companies have products that provide users with large amounts of data, they use standard approaches to help users manage that data, including filters, search, pagination, and Twitter’s choice, algorithm-defined organization. *Twitter also has some forms of filter and search, but the focus here will be on the algorithm.
With an algorithm, the company finds some way to calculate what any particular user might find interesting. Companies use these algorithms because they do show some benefit. They help drive conversion or engagement. But they are not complete solutions, for a few reasons.
First, these analytics help remove some noise (actually organize it) but don’t necessarily make the problem any better. There is no guarantee that signal is not also thrown away. There is also no guarantee that enough noise will be thrown away that a viable signal can be found. And most importantly, there is no way for users to know if the analytic has made the problem better or worse for them. They don’t provide users with any indication about why the tweets are defined as relevant and others as less relevant.