Attentive Multi-stage Learning for Early Risk Detection of Signs of Anorexia and Self-harm on Social Media

Waleed Ragheb (1) , Jérôme Azé (1) , Sandra Bringay (1, 2) , Maximilien Servajean (1, 2)

1 ADVANSE - ADVanced Analytics for data SciencE
2 UPVM - Université Paul-Valéry - Montpellier 3

Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia and self-harm respectively. The other one (T3) focus on estimation of the severity level of depression from a thread of user submissions. In this paper, we present the participation of LIRMM (Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks on early detection (T1 and T2). The proposed model addresses this problem by modeling the temporal mood variation detected from user posts through multistage learning phases. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-theart deep language models. The proposed models perform comparably to other contributions.

Mots clés: Classification; LSTM; Attention; Temporal Variation; Bayesian Variational Inference; Anorexia; Self-harm

Domaines: Systématique, phylogénie et taxonomie; Ecosystèmes; Biodiversité et Ecologie

Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean. Attentive Multi-stage Learning for Early Risk Detection of Signs of Anorexia and Self-harm on Social Media. CLEF 2019 - Conference and Labs of the Evaluation Forum, Sep 2019, Lugano, Switzerland. ⟨lirmm-03525311⟩