UDE at eRisk 2019: Early Risk Prediction on the Internet

Razan Masood (1) , Faneva Ramiandrisoa (2) , Ahmet Aker (1)

1 Universität Duisburg-Essen = University of Duisburg-Essen [Essen]
2 IRIT-SIG - Systèmes d’Informations Généralisées

In this paper, we describe our participation in CLEF eRisk workshop. eRisk 2019 is the third edition of this track3, which was first introduced in 2017. In the current edition, the organizers are targeting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks ofearly risk detection of Anorexia and self-harm. Our predictions are basedon Natural Language Processing using supervised machine learning with Support Vector Machines (SVM) and neural networks. SVM gave the best results among our five submitted models with latency-weighted F1of 0.58 andERDE5of 0.08 andERDE50of 0.04 for Anorexia detection task, while our more complicated neural network models did not show the desired performances.

Mots clés: Early risk detection; Anorexia; Self-harm; NLP; LSTM; SVM

Domaines: Recherche d'information [cs.IR]

Razan Masood, Faneva Ramiandrisoa, Ahmet Aker. UDE at eRisk 2019: Early Risk Prediction on the Internet. Conference and Labs of the Evaluation Forum, Living Labs (CLEF 2019), Sep 2019, Lugano, Switzerland. pp.1-9. ⟨hal-02942351⟩