A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community.

ABSTRACT: Globally, the number of people who suffer from depression is consistently increasing. Because both detecting and addressing the early stage of depression is one of the strongest factors for effective treatment, a number of scholars have attempted to examine how to detect and address early-stage depression. Recent studies have been focusing on the use of social media for depression detection where users express their thoughts and emotions freely. With this trend, we examine two-step approaches for early-stage depression detection. First, we propose a depression post-classification model using multiple languages Twitter datasets (Korean, English, and Japanese) to improve the applicability of the proposed model. Moreover, we built a depression lexicon for each language, which mental he

SUBMITTER: Cha J 

PROVIDER: S-EPMC9491270 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature