Bertha Brenes

Twitter explorer

For twitter I was using two queries:
1. Spanish = ’agroecologia, agroecologia, agricultura ecologica, agroecologico” there were 13 143 tweets
2. English = ’agroecology, agroecological, agriculture ecological” there were 296 656 tweets
For each query we actually get just data in english and Spanish so that give us a better scope for analyze the data separetly.

The Tools used and how I did it?

Get tweets can be easly and there is 2 option that will depends in your preference. The first option is to use the oficial Twitter API, but it will give you no more than 3500 tweets most recent and the second option and more convenient for me is to use use Twin library for scrap all the tweets, and also you delimited the period of time you want

1. Data with English Query

Tweet by time

We can see that the data in 2018 and 2019 increase a lot, and we still have to consider that 2019 is not finish and I made the scrapper in the begging of december

Map of who made the most common Term and Which Term

You can see in detail the map here

Map of the most common hashtags related with the user

There is a messy map that can tell us that most of the people almost use the same hashtags

You can see the map in detail here

The most commons hashtags

Here more organize table with the most common used hashtags

Which '@' are related to the hashtags

You can see in detail the map here

The Most common Terms in the Data

You can see in detail the map here
And you can see the terms extraction here

The geoMapping with the countries involves in the tweets

You can see the network in detail here

2. Data with Spanish Query

Tweet by Period

Even when the ammount of data is different, we can see the same pattern that in english data

Map of who made the most common Term and Which Term

You can see the network in detail here
That maps show te most commont terms founded in the data related with the user, is the color is strong is because this user write more about this term