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LinkedIn: the surprising effect of emojis on the virality of posts

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Should you put emojis in your LinkedIn posts, and if so, how many? In today’s article, I reveal the results of research I conducted on 4.599 million LinkedIn posts. This research follows the ones I’ve already published on the impact of hashtags and the word count.

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The results in a nutshell

  • Using 15 or 16 emojis in your LinkedIn post, you multiply by 2.5 the probability of getting 100 reactions. Simply put, the more emojis you put in your LinkedIn posts, the more visibility your post will get.
  • Based on 4,599m LinkedIn posts analyzed, we also observe that 80% of the posts do not contain emojis
  • 90.42% of LinkedIn posts contain 0, 1 or 2 emojis
  • 1.5% of LinkedIn posts contain more than 10 emojis.

banner emoticons emojis

Detailed results

The descriptive analysis of the dataset shows that 80.12% of the messages on LinkedIn do not contain emojis. This is a good illustration of Pareto’s law.

90.42% of LinkedIn posts contain 0, 1, or 2 emojis, and only 1.5% include more than 10. The maximum observed is a post containing 1190 emojis!

The prediction model performed with Timi Modeler shows that the average probability of getting 100 likes on a LinkedIn post is 3.5987%. I calculated this probability by limiting the observations to authors with a maximum of 25000 likes (i.e., 99% of the dataset). I wanted to eliminate the outliers (too many reactions) due to large networks characteristic of famous people (Barack Obama, Bill Gates, …). You will find in the table at the bottom of this article all the results.

I have taken the results using Tableau and eliminated the outlier related to the post containing 1190 emojis.

I also included (in orange below) the percentage of posts according to the number of emojis contained.

 

A polynomial function gives a perfect “fit” (R²=0.96, see blue dotted line), and we see that an asymptote is reached around 18 emojis. So, there is no need to include many more.effect of emoticons on Linkedin posts viralityA polynomial function gives a perfect “fit” (R²=0.96, see blue dotted line), and we see that an asymptote is reached around 18 emojis. So, there is no need to include many more.

Here are the essential lessons I learned from this analysis:

  • not including emojis statistically decreases your chances of getting 100 likes.
  • 15 or 16 emojis seem to be the optimum to increase your chances of getting 100 likes according to the prediction model, and 18 if I follow the regression realized using Tableau.
  • 80% of the messages on LinkedIn do not contain emojis: putting them on allows you to differentiate yourself

As for the other analyses I published, the question of causality remains essential. At this stage, it is impossible to say what causes the reactions. Does LinkedIn’s algorithm “push” more publications that contain emojis, or is the human eye more attracted to publications that have them? A lot of work is still needed to solve this mystery and maybe even some research on eye tracking.


Methodology

To realize this research, I used a dataset of 4,599 million LinkedIn posts provided by Linkalyze. I prepared the data with Anatella, and the modeling was realized using Modeller (these 2 solutions being supplied by Timi).

data preparation Linkedin dataset Anatella

The data preparation part mainly consisted of the reconciliation of 2 databases in JSON format (see diagram below):

  • LinkedIn posts database
  • authors database

As the emojis are not directly readable, a regular expression has been used to extract and count them. For those who want to reuse it, here it is:

/(?:[\u2000-\u32FF]|[\ud83c-\ud83e][\udc00-\udfff])(?:\u200d(?:[\u2000-\u32FF]|[\ud83c-\ud83e][\udc00-\udfff]))*/g

It was then necessary to manually eliminate other signs that were also extracted but were not emojis.

The modeling consisted of predicting the probability of reaching 100 reactions with a LinkedIn post (Likes + comments). To do this, I created a “ManyReactions” variable that identifies posts that have reached the symbolic 100 reactions mark.

In the modeling, “ManyReactions” becomes the dependent variable. All the other variables of the dataset become independent variables.

variable many reactions dans timi modeler


Correlation between the number of emojis on a LinkedIn post and the probability of virality

Number of emoticons Probability of getting 100 likes on the LinkedIn post
0 3,09%
1 4,24%
2 4,85%
3 5,46%
4 5,93%
5 6,77%
6 6,98%
7 7,58%
8 8,12%
9 8,82%
10 8,69%
11 8,80%
12 8,81%
13 8,86%
14 9,55%
15-16 10,38%
17-18 9,96%
19-21 9,99%
22-28 9,88%
29-1190 7,13%

 

 


Acknowledgments

Thanks to Frank Vanden Berghen for his help in programming the emoji extraction feature.

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Author: 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)

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1 Comment

  1. I have emojis in front of my name and recently did this for a business too. I am now being told that having emojis in the “name field” can disrupt the LinkedIn algorithm and search. Can you please confirm or repute this? Thank you.

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