An Exploratory Analysis of the Relation Between Offensive Language and Mental Health
This work addresses the problem of understanding mental health indicators in online behavior for researchers in computational linguistics and psychology, but it is incremental as it builds on existing datasets and methods.
The paper tackled the relationship between offensive language and mental health by analyzing social media posts, finding that offensive language is more frequently used in samples from individuals with self-reported depression or signs of depression.
In this paper, we analyze the interplay between the use of offensive language and mental health. We acquired publicly available datasets created for offensive language identification and depression detection and we train computational models to compare the use of offensive language in social media posts written by groups of individuals with and without self-reported depression diagnosis. We also look at samples written by groups of individuals whose posts show signs of depression according to recent related studies. Our analysis indicates that offensive language is more frequently used in the samples written by individuals with self-reported depression as well as individuals showing signs of depression. The results discussed here open new avenues in research in politeness/offensiveness and mental health.