Linkedin algorithm: 1 reaction will get you 83 views

How does Linkedin algorithm influence? What is the correlation between the number of likes or comments and the number of views in LinkedIn?

Everybody’s trying to understand LinkedIn’s algorithm. Some are even trying to hack it.

While theories abound, no one knows how it works. I’ve rolled up my sleeves, and today I’m going to explain it to you, with a statistical model to back it up.

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Summary

• Introduction
• Methodology
• Results
• Limitations

Introduction

The LinkedIn algorithm remains somewhat of a mystery. A common hypothesis is that the number of reactions (like, curiosity, see opposite) attributed in the first 2 hours of publication is crucial to make the publication go viral. However, since the update of the Linkedin algorithm, this hypothesis may be questionable.

The hypotheses, therefore, remain general and no studies have been made on the propagating effect of reactions and comments. In other words, how many more people are likely to be affected when someone reacts or comments on your LinkedIn post?

I will reveal all in today’s article.

Methodology

To build the dataset, I went back as far as I could in my posts to note :

• the number of views
• the variables characterising the post (presence of a hyperlink, a photo, a video, the text of the post, hashtags, …)

I managed to obtain 17 months of retrospective analysis.

The final dataset consists of 257 posts, spread over 17 months, coded according to 17 variables. In the end, we have a small dataset containing 4369 posts. It’s small, but the effort (manual) to build this dataset is quite important (about 10 hours of work).

For the analysis, I used Anatella for data preparation, Tableau Software for data visualisation, and R for statistical analysis (via integration in Anatella).

Data Preparation Process using Anatella.

The preparation of the data is done thanks to a simple flow (see above) in which I cleaned the data, eliminated empty fields, and created a variable “comment_reactions” which is the sum of the number of reactions and the number of comments.
Different types of basic models have been tested to simulate the number of views (dependent variable). A polynomial model gave the best results.

Results

Here’s the part for which you’ve all been waiting. What is the relationship between the number of reactions/comments and the number of views?

The graph below shows you the result of the modelling. You can see that even without likes and comments, a publication should still reach 303 people. Each like or comment brings an increment of 83 additional views. Beyond 100 likes+comments, the model marks an inflexion which is mainly due to the absence of measurement points (only 2).

The number of views on LinkedIn based on the number of likes and comments. Polynomial model (degree 2) made with the GGplot (R) library via Anatella.

The trend line in Tableau gives roughly the same result (Tableau shows an R² of 0.78; Anatella a pseudo-R² of 0.72). I have added some additional dimensions in the table view: presence of photo in the post: red when there are no photos, green when the number of photos is >1.

The size of the measuring points represents the number of characters in the LinkedIn post. The larger the “bubble”, the longer was the LinkedIn post.

Limitations

Network effects are not taken into consideration in this study. The date and time of publication are also not taken into account because the “timestamp” of the publications is not available.

There are few measurement points for large view values. It is, therefore, necessary to increase the size of the dataset with very popular (viral) publications to remodel the set.

The analysis only concerns one person (me), which is, of course, the most critical limitation. If you wish to contribute to this research effort, contact me, and I will do the statistical analysis.

By Pierre-Nicolas Schwab Pierre-Nicolas est Docteur en Marketing et dirige l'agence d'études de marché IntoTheMinds. Ses domaines de prédilection sont le BigData l'e-commerce, le commerce de proximité, l'HoReCa et la logistique. Il est également chercheur en marketing à l'Université Libre de Bruxelles et sert de coach et formateur à plusieurs organisations et institutions publiques. Il peut être contacté par email, Linkedin ou par téléphone (+32 486 42 79 42)