Paris Match, Louvre, Le Monde, OM, and my french language

 

Paris Match 500 words

Paris Match 500 words

I thought of exploring something new in my lexical and social networks trips. The alternative way to learn new languages’ vocabulary by immersing in a contexts of interest to the learner. This is well known for educators and language facilitators. But what can add to this field with a possible realization of the potentials social networks can bring here?

 

So i went on Facebook network, and I explored the language coming from four french pages on Facebook. By French I mean the entity owning the page is a french one. Just like OM | OLYMPIQUE DE MARSEILLE. I did that on the tree consecutive days as i was busy with some other stuff.

My main objective was to note the french vocabulary used on these four networks. And, for this quick work I allocate only a couple of hours as all I wanted was just to have a first look and not to dig any further.  To do so, i needed to go two steps:

First: do some quantitive research on the most repeated (100 and 500) words used in fans comments on the last (100) posts by each one of these pages. A post could be a link, photo, video, or text status.

Second: to visualize the result in word cloud style. Just to be gentle with the eyes.

So let us now have a look together.

Part one – Data:

Network OM (Sport) Paris Match (Journalism) Le Monde (Journalism) Louvre (Museum)
Date: 31-Jul-15 2-Aug-15 3-Aug-15 3-Aug-15
Fans: 4,800,000 664,620 2,415,204 1,760,715
Posts: 100 100 100 100
Engaged users: 168809 39555 76826 143637
Liking or commenting: 745006 99875 148354 482520
Comments: 25095 5629 18798 5911
Likes: 719911 94246 129556 476609
Engaged fans % 3.52% 5.95% 3.18% 8.16%

In fact, this table is more helpful to have some notes related to media engagement rather than the linguistic work. So i am not going to discuss it here, as it belongs to another research i started in June/July 2014 about media outlets online. (NewYork times, France 24, Huffington Post, Al Jazeera, etc.). Yet what is interesting here is the users engagement ratio. as it shows for example how Paris Match is enjoying more engaged readers than Le Monde. That is one of the strong indications about quantity of fans vs. engagement of fans. Another could be interesting note is to realize how Louvre, being a cultural entity is enjoying relatively strong engagement, question is whether this engagement is leaned more towards tourism or art. to discover this one needs to compare likes to comments ratio in the first place and then do some qualitative analysis in the comments themselves.

Part Two – Visualizing:

I am going to post here the words clouds coming from the four sources (OM, Le Monde, Louvre, Paris Match) in this same order and two images per each. One visualizing the most frequent 100 words and one is visualizing most frequent 500 words.

OM 100 words

OM 100 words

OM 500 words

OM 500 words

Le Monde 100 words

Le Monde 100 words

Le Monde 500 words

Le Monde 500 words

Louvre 100 words

Louvre 100 words

Louvre 500 words

Louvre 500 words

Paris Match 100 words

Paris Match 100 words

Paris Match 500 words

Paris Match 500 words

 

So what is next?

For language learning purposes what we can do is to consider three notes:

1- Using stop lists to generate the graphs. A stop list is a list of words that you ask that you block from appearing in the visualizing or to be considered in the counting and latter analytical work. Such words can be “de, cette, par, les, tout, plus, un. .. all, from, this, that, more, etc.” this will allow for a better visualizing to what is called “concept” words. words that are related to the concept we are searching (Journalism, Art, Sport, Culture, Business, Politics..etc.)

2- But still, the work here confirms the importance of the non conceptual words. In fact these words consist 40% – 50% of languages. learning these words is half away towards learning a language! or at east reading it / recognizing it. Off course it will not solve the syntactic and semantic aspects of language constructing (which is in simpler words : making a proper and meaningful sentence) but  it is still helpful and break the ice with the language and its speakers.

3- For language experts one can rely now social networks for a more rewarding NLP/ Natural Language Processing work.

 

Finally, for me, it was interesting to continue my work on studying the richness of the language and how it is related to personality type, and personal interests. Alors, j’ai appris quelques mots nouveaux :)

Ok, that was all for this post, I hope you enjoyed it.

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