  In this paper, we present our participation to CLEF MC2 2018 edition for the
task 2 Mining opinion argumentation. It consists in detecting the most
argumentative and diverse Tweets about some festivals in English and French
from a massive multilingual collection. We measure argumentativity of a Tweet
computing the amount of argumentation compounds it contains. We consider
argumentation compounds as a combination between opinion expression and its
support with facts and a particular structuration. Regarding diversity, we
consider the amount of festival aspects covered by Tweets. An initial step
filters the original dataset to fit the language and topic requirements of the
task. Then, we compute and integrate linguistic descriptors to detect claims
and their respective justifications in Tweets. The final step extracts the most
diverse arguments by clustering Tweets according to their textual content and
selecting the most argumentative ones from each cluster. We conclude the paper
describing the different ways we combined the descriptors among the different
runs we submitted and discussing their results.
