🇪🇺 Key Opinion leaders ⚽

Recently, the failed #EuropeanSuperLeague project set #Twitter on fire.

Between the millions of tweets published on the subject, some voices had a particularly important weight.

Here are the #KOL of European football according to #data:

This thread is the continuation of the analysis I shared on the impact of Twitter on the Super League collapse.

The #dataset used is the same: 2.6 million tweets posted by 876,000 unique users in 5 languages (🇬🇧 🇫🇷 🇪🇦 🇮🇹 🇩🇪 )

In my first analysis, I focused on the contents (sentiment analysis, semantic cartographies...), here the goal is to analyze the influential content providers in Europe.
A little methodological point: when we talk about #influencers or #KOL, we can categorize them according to different #KPI (indicators).
Here, two standpoints are used:
First I will analyze the mentions thanks to cartographies, then the engagement thanks to interactive graphs.
All #dataviz are available in interactive format here:


I strongly encourage you to go and dive into the differents 3D interactive graphs : you can navigate into them and find opinion leaders that correspond to your criteria !
The analysis of the mentions highlights the opinion leaders solicited.

By sampling my dataset on the English community, here is the cartography of actors I get.

The actors' mapping highlights different communities:
🔴 Super League member clubs
🔵 Official institutions
🟤 English media
🟣 Specialized information relays
🟢 Players
🟡 Clubs not participating to the project
The first communities are clubs and institutions (🔴🔵🟡): the subject of the controversy is concerning them, their majority presence is logical.

What remains interesting is to see which clubs are the most mentioned:
🥇 @ManUtd
🥈 @LFC
🥉 @Arsenal
When we focus on the "media" community, we can see that the presence of English sports media such as @SkySports on Twitter is carried by former players now consultants:
🥇 @GNev2
🥈 @Carra23
🥉 @GaryLineker
Focusing on specialized information relays, this community is carried by some international accounts:
🥇 @FabrizioRomano
🥈 @elchiringuitotv
🥉 @TheAthleticUK
Obviously, if there are people in the football ecosystem that the supporters were waiting to hear, it's the players!
In our list of mentions we must distinguish two types of players:
◾ Players who spoke up and got a reaction:
🥇 @MarcusRashford
🥈 @jhenderson
🥉 @B_Fernandes8 (posting on Instagram)

◾ Players who didn't speak up:
🥇 @Cristiano
🥈 @hkane
🥉 @KMbappe

The last communities of the map highlights that the English fans were waiting for a political response:
On one side (blue), we find for example: @BorisJohnson or @Conservatives
On the other (purple), online petition platforms: @UKChange or @Change
Looking at the same dataset, but with the angle of engagement (RT and likes), the #KOL vary.
And this is logical!
Here, we are more into a qualitative approach: we analyze the actors whose voice is supported.
Here are two graphs showing the Top Users according to two parameters: RT on the right and Likes on the left.

An interactive 3D fusion of these graphs is available here: bit.ly/ENG_RTT
These graphs represent 3 variables: the number of publications on the y-axis (count), the volume of engagement on the x-axis and size (RT or likes) and the ratio for the color (Ratio RT or Ratio Likes).
These combined graphs allow us to determine the engagement leaders on English football Twitter:
🥇 @fabrizioromano
🥈 @brfootball
🥉 Skysports: (@skysportsnews & @skysportspl)
To conclude this analysis and to answer the initial question (who are the KOLs of football in Europe), here are the engagement graphs on my complete dataset:

3D here : bit.ly/UE_RTT
When you look at the cumulative engagement:

👑The most influential news relay on Twitter is, by far, @FabrizioRomano!

📰The most influential media is @Skysports with its two accounts: @Skysportsnews & @SkysportsPL
If we focus on the Engagement Ratio per post:

📈@brfootball and @trollfootball accounts stand out in terms of RT Ratio.

@GaryLineker is the most influential actor in terms of Likes ratio.
Media focused in a particular club also generate huge engagement in Europe:

📊@utdreport & @theMadridZone
Finally, if we look at this datavisualization by highlighting the language used by the users, here is what we get:

3D here : bit.ly/UE_lang
🇬🇧 We can see that English content is by far the most engaging.

🇫🇷 However, the French information accounts such as @ActuFoot_ manage to generate a very strong engagement.

🇪🇸 For Spain, the @chiringuitotv and @2010MisterChip are the most influential accounts.
If you have any question or if you want to analyze the data of a particular subject, you can contact me on Twitter ! ;)

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More from @nicolasbchb

7 May
🇪🇺 #EuropeanSuperLeague

Using my #data analysis tools, I tried to measure what role Twitter played in this failure:

Without this network, would the closed league project, led by the 12 richest clubs in Europe, have had the same fate?

To analyze what happened and answer this question, I extracted a large amount of data:

2.6 million tweets posted by 876,000 unique users
5 targeted languages : 🇫🇷 🇬🇧 🇪🇦 🇮🇹 🇩🇪
56 million likes and 7.8 million retweets
The tweets collected start from April 17th at noon to April 22nd at midnight.

I'm going to go back over the facts chronologically so that we can relive this story from the Twitter standpoint.

First of all, here is the distribution of the tweets I collected on the period:
Read 34 tweets
5 May
🇪🇺 Leaders d'opinion ⚽

Récemment, le projet avorté #EuropeanSuperLeague a fait s'enflammer #Twitter.

Au cœur des millions de tweets publiés sur le sujet, des voix ont un poids particulièrement important.

Voici les #KOL du foot européen selon la #data :

Ce thread est la suite de l'analyse que j'avais partagé sur l'impact de Twitter dans l'abandon de la Super League.

Le #dataset utilisé est le même : 2,6 millions de tweets postés par 876 000 users uniques dans 5 langues (🇫🇷 🇬🇧 🇪🇦 🇮🇹 🇩🇪 )

Là où dans la première analyse je me consacrais sur les contenus (sentiment analysis, cartographies sémantiques...), ici l'objectif est d'analyser les émetteurs de contenu influents.
Read 25 tweets
30 Apr
🇪🇺 #EuropeanSuperLeague

En utilisant mes outils de #DataScience, j'ai essayé de mesurer quel rôle a joué #Twitter dans cet échec :

Sans ce réseau, le projet de ligue fermée mené par les 12 clubs les plus riches d'Europe aurait-il vécu le même sort ?

Pour analyser ce qu'il s'est passé et répondre à ma question, j'ai extrait une grande quantité de données :

2,6 millions de tweets postés par 876 000 users uniques
5 langues ciblées : 🇫🇷 🇬🇧 🇪🇦 🇮🇹 🇩🇪
56 millions de likes et 7,8 millions de retweets cumulés Image
Les tweets récoltés vont du 17 avril midi, jusqu'au 22 avril minuit.

Je vais revenir sur les faits chronologiquement pour qu'on revive cette histoire en suivant l'angle Twitter.

Voici tout d'abord la répartition des tweets que j'ai récupéré sur la période : Image
Read 34 tweets

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