Crosslingual Depression Detection in Twitter Using Bilingual Word Alignments

Abstract

Depression is a mental disorder with strong social and economic implications. Due to its relevance, recently several researches have explored the analysis of social media content to identify and track depressed users. Most approaches follow a supervised learning strategy supported on the availability of labeled training data. Unfortunately, acquiring such data is very complex and costly. To handle this problem, in this paper we propose a crosslingual approach based on the idea that data already labeled in a specific language can be leveraged to classify depression in other languages. The proposed method is based on a word-level alignment process. Particularly, we propose two representations for the alignment; one of them takes advantage of the psycholinguistic resource LIWC and the other uses bilingual word embeddings. For evaluating the proposed approach, we faced the detection of depression by employing English and Spanish tweets as the source and target data respectively. The results outperformed solutions based on automatic translation of texts, confirming the usefulness of the proposed approach.