Twitter is perhaps the social media more amenable for research. It requires only a few steps to obtain information, and there are plenty of libraries that can help in this regard. Nonetheless, knowing whether a particular event is expressed on Twitter is a challenging task that requires a considerable collection of tweets. This proposal aims to facilitate,  a researcher interested in Twitter data , the process of mining events on Twitter. The events could be related to natural disasters, health issues,  people's mobility, among other studies that can be pursued with the library proposed. Different applications are presented in this contribution to illustrate the library's capabilities , starting from an exploratory analysis of the topics discovered in tweets, following it by studying the similarity among dialects of the Spanish language, and complementing it with a mobility report on different countries. In summary, the Python library presented  retrieves a plethora of information processed from Twitter (since December 2015) in terms of words, bigrams of words, and their frequencies by day for Arabic, English, Spanish, and Russian languages. Finally, the mobility information considered is related to the number of travels among locations for more than 245 countries or territories  .
Twitter is perhaps the social media more amenable for research. It requires only a few steps to obtain information, and there are plenty of libraries that can help in this regard. Nonetheless, knowing whether a particular event is expressed on Twitter is a challenging task that requires a considerable collection of tweets. This proposal aims to facilitate, to a researcher interested , the process of mining events on Twitter. The events could be related to natural disasters, health issues, and people's mobility, among other studies that can be pursued with the library proposed. Different applications are presented in this contribution to illustrate the library's capabilities : an exploratory analysis of the topics discovered in tweets, a study on similarity among dialects of the Spanish language, and a mobility report on different countries. In summary, the Python library presented is applied to different domains and retrieves a plethora of information processed from Twitter (since December 2015) in terms of words, bi-grams of words, and their frequencies by day for Arabic, English, Spanish, and Russian languages. The mobility information is related to the number of travels among locations for more than 200 countries or territories ; our library also provides access to this information .
A
to
D
in Twitter data
A
and
R
, starting from
:
R
following it by studying the
a study on
D
complementing it with
A
is applied to different domains and
R
bigrams
bi-grams
R
Finally, the mobility information considered
The mobility information
R
245
200
A
; our library also provides access to this information