LinkedIn has shared a new technical overview of its efforts to combat viral spam in the app, which also provides some interesting notes on how its feed algorithm works, and how content gains traction in the app.

Which could help in your strategic planning – or at least, it'll help you understand the factors that weigh into LinkedIn's algorithmic flow, which ultimately dictates post reach.

In fact, LinkedIn describes that it's not built to be the kind of social app which spreads virality like all other apps:

"LinkedIn is not built for virality but sometimes posts which generate huge engagement in terms of likes, reactions, comments, and reshares within a short span of time may be deemed viral."

LinkedIn is more community and niche-related, so boosting all the most popular posts doesn't really fit in the framework of the app. However, posts that create a mountain of engagement will still be shared more broadly as a result-and of course, everyone trying to maximize their performance in the app is working towards post optimization, however they can.

So how do you optimize post reach?

In the introduction, LinkedIn describes how their system identifies potentially viral content, and halts potentially violative posts:

"As soon as a piece of content surfaces, the existing ML classifiers act based on the immediate features that can be computed, such as author and content related features.". If it is spam or policy-violating, then we either take an automatic action or send it for human review to decide on the action to be taken. For the content that is still present on the platform, we monitor the engagement signals, temporal signals, and spam related signals to detect the potential for viral spam during the content lifecycle on the platform.
So, LinkedIn tells us that the four most critical factors which affect post performance are:

The post author
Engagement signals
Temporal signals (velocity of likes/reactions, shares, comments, and views)
And, about post author, LinkedIn claims its algorithm counts:

The influence and popularity of [members posting and engaging with a post] as their action might expose the post to a lot more members creating a cascade effect which makes the post go viral. Here, we use features such as followers and connection counts, diversity in industry, location, and level of the network (connections and followers) of these members.

It's worth noting that LinkedIn calls people 'members' instead of 'users', because the site does not publish a user count, but publishes the number of members.

Engagement signals
According to LinkedIn, it measures likes and reactions on every post, as well as shares, comments, and views.
"We infer different kinds of features, like the time sequence of counts and the velocity of likes, reactions, shares, comments, and views. These are the strongest indicators that a cascading process is actually happening in the network."

So velocity is important, but what factors really contribute to maximizing your mileage on LinkedIn? You would expect it is something like the following:

The number of followers you have
The number of people you're connected to
Diversity considerations-or at least, it is very vague)
Your location
The age of your network users
Engagement speed with the post
LinkedIn doesn't state that either likes or comments or shares have a heavier influence, but that's probably another factor in its ranking system as well.

So, it's better to start with your audience on LinkedIn and hopefully most of them will be followers. Follower counts, of course, count for more than basic connections, though both are factors-but also it is worth noting that once someone has connected with you, they can unfollow you and remain a connection.

You can find your follower count in the settings of your LinkedIn feed.

Then, of course, you just have to post interesting content. Which is not necessarily easy but monitoring your feed and learning what works for others might get you a better view on posting best practices. Here's an overview of the most shared LinkedIn posts in 2022:.

Regarding spam detection - its core subject matter - the firm says systematic updates have led to marked improvements in detecting and removing violative content generally, with the overall percentage of views on spam declining by 7.3 percent.

So it's improving its systems while giving some extra insight into workings of its algorithm.