Early Mental Health Risk Assessment through Writing Styles, Topics and Neural Models Diego Maupom´ e, Maxime D. Armstrong, Raouf Belbahar, Josselin Alezot, Rhon Balassiano, Marc Queudot, S´ ebastien Mosser [0000-0001-9769-216X] , and Marie-Jean Meurs [0000-0001-8196-2153] University of Quebec in Montreal UQAM meurs.marie-jean@uqam.ca Abstract. This paper describes the participation of the RELAI team in the eRisk 2020 tasks. The 2020 edition of eRisk proposed two tasks: (T1) Early assessment of risk of self-harm and (T2) Signs of depression in social media users. The second task focused on automatically filling a depression questionnaire given user writing history. The RELAI team participated in both tasks, and addressed them using topic modeling al- gorithms (LDA and Anchor), neural models with three different architec- tures (Deep Averaging Networks (DANs), Contextualizers, and Recur- rent Neural Networks (RNNs)), and an approach based on writing styles. For the second task related to early detection of depression, the system based on LDA performed well according to all the evaluation metrics, and achieved the best performance among participants according to the Average Difference between Overall Depression Levels (ADODL) with a score of 83.15%. Overall, the submitted systems achieved promising results, and suggest that evidence extracted from social media could be useful for early mental health risk assessment. Keywords: Early Risk Detection · Topic Modeling · Neural Networks · Mental Health Risk Assessment. 1 Introduction The global goal of the eRisk challenges is the early detection of at-risk people from their textual production on social media, using Natural Language Process- ing (NLP) techniques. In 2020, two different tasks were put forth: early detection of signs of self-harm (T1), and measuring the severity of the signs of depres- sion (T2) using textual data from related Reddit subreddits 1 . These tasks are follow-ups of tasks 2 and 3 from 2019, respectively. T1 consists in sequentially Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CLEF 2020, 22-25 September 2020, Thessaloniki, Greece. 1 https://www.reddit.com.