Le traitement de données provenant de réseaux sociaux en temps réel est devenu une outil attractifdans les situations d’urgence, mais la surcharge d’informations reste un défi à relever. Dans cet article,nous présentons un nouveau jeu de données en français annoté manuellement pour la gestion de crise.Nous testons également plusieurs modèles d’apprentissage automatique pour classer des tweets enfonction de leur pertinence, de l’urgence et de l’intention qu’ils véhiculent afin d’aider au mieux lesservices de secours durant les crises selon des méthodes d’évaluation spécifique à la gestion de crise.Nous évaluons également nos modèles lorsqu’ils sont confrontés à de nouvelles crises ou même denouveaux types de crises, avec des résultats encourageants

The annotation task we elaborated aims at describing the contextual factors that influence the appearance and interpretation of moral predicates, in newspaper articles on police brutality, in French and in English. The paper provides a brief review of the literature on moral predicates and their relation with context. The paper also describes the elaboration of the corpus and the ontology. Our hypothesis is that the use of moral adjectives and their appearance in context could change depending on the political orientation of the journal. We elaborated an annotation task to investigate the precise contexts discussed in articles on police brutality. The paper concludes by describing the study and the annotation task in details.

Discovered by (Austin,1962) and extensively promoted by (Searle, 1975), speech acts (SA) have been the object of extensive discussion in the philosophical and the linguistic literature, as well as in computational linguistics where the detection of SA have shown to be an important step in many down stream NLP applications. In this paper, we attempt to measure for the first time the role of SA on urgency detection in tweets, focusing on natural disasters. Indeed, SA are particularly relevant to identify intentions, desires, plans and preferences towards action, providing therefore actionable information that will help to set priorities for the human teams and decide appropriate rescue actions. To this end, we come up here with four main contributions: (1) A two-layer annotation scheme of SA both at the tweet and subtweet levels, (2) A new French dataset of 6,669 tweets annotated for both urgency and SA, (3) An in-depth analysis of the annotation campaign, highlighting the correlation between SA and urgency categories, and (4) A set of deep learning experiments to detect SA in a crisis corpus. Our results show that SA are correlated with urgency which is a first important step towards SA-aware NLP-based crisis management on social media.

In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.

Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper presents the first French corpus annotated for sexism detection composed of about 12,000 tweets. In a context of offensive content mediation on social media now regulated by European laws, we think that it is important to be able to detect automatically not only sexist content but also to identify if a message with a sexist content is really sexist (i.e. addressed to a woman or describing a woman or women in general) or is a story of sexism experienced by a woman. This point is the novelty of our annotation scheme. We also propose some preliminary results for sexism detection obtained with a deep learning approach. Our experiments show encouraging results.