Rule Based Method for Terrorism, Violence and Threat Classification: Application to Arabic Tweets In this paper, we present a rule based method to classify Tweets under three main categories; terrorism, violence and threat classes. Given that Arabic is a morphologically complex language, we build a linguistic module to identify a set of patterns for each class. Our proposed method requires three fundamental steps: First, we create our reference corpus collected from Arabic tweets. From the study of this corpus, we identify a set of linguistic rules. Finally, these patterns will be rewritten into local grammar within the linguistic platform NooJ. The evaluation of our system achieved encouraging results to obtaining 84%, 86.8% and 84.7% in terms of recall, precision and fscore respectively, when applied to test corpus.

IDAT at FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets This overview paper describes the first shared task on irony detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5, 030 Arabic tweets about different political issues and events related to the Middle East and the Maghreb. Tweets in our dataset are written in Modern Standard Arabic but also in different Arabic language varieties including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams registered to the task among which ten submitted their runs. The methods of participants ranged from featurebased to neural networks using either classical machine learning techniques or ensemble methods. The best performing system achieved Fscore value of 0.844, showing that classical featurebased models outperform the neural ones.