LinkedIn is a popular social media platform used by professionals and companies to network, share content, and build their brand. One way users can interact with content on LinkedIn is by liking posts and comments. However, some users have questioned whether the order of likes on LinkedIn is manipulated or if it accurately shows when people liked the content. In this article, we will examine if LinkedIn likes are displayed chronologically or if other factors influence the order.
How LinkedIn Displays Likes
When you view the list of likes on a LinkedIn post or comment, they appear in a seemingly random order, not sorted by time. You cannot see exactly when each person liked the content. This contrasts with some other social platforms like Facebook that show likes in strict reverse chronological order.
LinkedIn has confirmed that likes are intentionally not displayed in chronological order. Instead, their algorithm considers several factors to determine the sequence of likes including:
- How recently the like occurred
- The strength of connection between the liker’s profile and the post author’s profile
- How influential or prominent the liker’s profile is
LinkedIn says displaying likes this way helps highlight and promote engagement from close connections and influential profiles first. It aims to show users the most relevant likes according to their professional network.
Why LinkedIn Orders Likes This Way
There are several potential reasons why LinkedIn decided not to show likes in chronological order:
- To emphasize likes from users who are closely connected to the post creator. Seeing engagement from co-workers, close connections, and influencers can inspire more likes.
- To reduce the bandwagon effect. If likes appeared chronologically, early likes could snowball as users simply like what others have already liked.
- To prevent misconceptions about popularity. If early likes came mostly from distant connections, it may wrongly seem less popular.
- To sustain engagement. Spreading out influential profiles’ likes over time keeps driving likes longer.
Pros of LinkedIn’s Approach
Displaying likes based on relevance instead of time has some benefits:
- Promotes engagement from influential profiles. Seeing early likes from important connections can increase overall engagement andsharing. It utilizes the power of reputation.
- Prevents misconceptions about post popularity. If distant relations like first, it may wrongly seem less relevant to your closer network.
- Sustains engagement over time. Spreading out likes avoids early spikes and drops. The momentum lasts longer.
- Encourages users to read more of the post. Sorting chronologically often leads users to just look at the first few likes. LinkedIn’s approach gets users scanning more of the list.
Cons of LinkedIn’s Approach
However, LinkedIn’s system for ordering likes also has some drawbacks:
- Reduced transparency. Users cannot clearly see when each person liked or the true chronological sequence. It feels less open.
- Algorithms can make mistakes. The formula guessing relevance of likes/likers may misjudge importance to the user.
- Gives excessive influence to influencers. Prioritizing likes from influential profiles risks skewing perceptions of relevance.
- Harder to spot suspicious activity. Not seeing chronological order makes it harder to notice if there are ever sudden spikes of questionable likes.
The Case for Chronological Order
Here are some advantages if LinkedIn displayed likes in strict chronological order like some other platforms:
- It feels more transparent when users see the exact timing of each like.
- The sequence tells a story about how reaction spread to the post.
- Users can better spot any sudden surges of engagement that may seem suspicious or inorganic.
- Algorithms don’t dictate which likes feel most relevant to each user.
- Early influencer likes don’t over-impact perceptions about a post’s importance.
How Post Creators Are Impacted
For creators and publishers of content on LinkedIn, the algorithmic sorting of likes influences perceptions of their content in a few key ways:
- Seeing early likes from close connections helps build momentum through the creator’s inner circle.
- But influencer likes being spread out sustains momentum versus brief initial spikes.
- On the other hand, it makes it harder to know if brief spikes are driven by suspicious forces.
- There is some dependence on algorithms to surface the most relevant/helpful likes.
- The order doesn’t fully show how interest spread through the creator’s wider network over time.
Do LinkedIn Likes Impact Rankings?
Likes are an important signal that the LinkedIn algorithm considers when ranking content in users’ feeds and in search results. However, LinkedIn has not provided specifics on how likes factor into rankings versus other signals like comments, shares, clicks, and post age.
We do know that more likes help increase reach. But the order likes are displayed does not itself impact how much each like counts for rankings. It is the total number of likes that matters most. The sequence does however influence perceptions of engagement.
Types of Content that Gets More Likes
Certain types of posts and content tend to reliably attract more likes on LinkedIn:
- Industry news and top headlines get lots of early likes frommany people.
- Posts with link previews get more likes because it’s easier to quickly see what it’s about.
- Lists and slideshows attract lots of likes as they are easy to skim through and engage.
- Quizzes, polls, and surveys get lots of likes due to curiosity and interactivity.
- Behind-the-scenes company photos and videos generate likes from employees.
Tips to Get More LinkedIn Likes
Here are some tips creators and companies can use to help generate more likes on LinkedIn posts:
- Post consistently. Don’t let your profile go silent for long stretches.
- Post at optimal times based on insights. Track when your followers are most active.
- Use strong visuals including images, video, and presentations.
- Write concise posts that are easy to quickly like and comment on.
- Create content specifically designed to entice likes such as lists, quizzes, and polls.
- Ask questions to spark more comments and post interactions.
- Engage with your posts after publishing by liking and replying to comments.
- Leverage influencers by tagging them or sharing their content and letting them know.
- Run giveaways and contests requiring users to like posts in order to enter.
Should LinkedIn Switch to Chronological Order?
There are reasonable arguments on both sides of whether LinkedIn should change to chronological order for likes or not. Here are a few key questions to consider:
- Would transparent chronological order increase perceptions of authenticity and trustworthiness?
- How much does the current system skew creator’s perceptions of engagement on their posts?
- Would creators lose out on momentum from influencer likes being spread out over time?
- Could strict chronological order enable gaming the system by timing likes?
- How difficult would it be for LinkedIn to change this embedded system?
The choice likely comes down to whether LinkedIn believes transparency overrides their current goals for how to showcase the most relevant engagement first. So far, LinkedIn has not shown signs of changing their approach.
Conclusion
LinkedIn purposefully does not display likes in chronological order. Instead their algorithm curates likes based on relevance factors. The goal is to highlight engagement from influential profiles and close connections first. This sustain engagement momentum and focuses users on seeing the most important likes according to LinkedIn’s calculations.
The approach has advantages but also criticisms around lack of transparency, giving algorithms too much influence, and increased difficulty spotting suspicious engagement spikes. While a chronological system would address some of those concerns, LinkedIn seems to believe relevance should still trump pure chronology for now. Creators and companies using LinkedIn simply need to understand how the platform showcases likes to accurately interpret their posts’ reach.