Abstract
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.
Highlights
The use of offensive language is pervasive in social media and it has been studied from different perspectives
A popular line of research is the study of computational models to identify offensive content online relying on traditional machine learning classifiers (Xu et al, 2012; Dadvar et al, 2013), neural networks (e.g. LSTMs, GRUs) with word embeddings (Aroyehun and Gelbukh, 2018; Majumder et al, 2018), and more recently, transformer models like ELMO (Peters et al, 2018) and BERT (Devlin et al, 2019) which have shown to obtain competitive scores topping the leaderboards in recent shared tasks on offensive language and hate speech detection (Liu et al, 2019)
Using the Hs heuristic, we demonstrate that there is a statistically significant difference (Welch t-test, p-value
Summary
The use of offensive language is pervasive in social media and it has been studied from different perspectives. Computational models have been applied to identify the various types of offensive content (Basile et al, 2019) and to, for example, study the relation between profanity and hate speech (Malmasi and Zampieri, 2018) and the different functions and intentions of vulgarity in social media (Holgate et al, 2018). Most of the datasets used in the aforementioned studies contain data sampled from the general population and very little light has been shed on the use of offensive language in online communication by specific groups such as individuals with mental health conditions. A notable exception is the recent study by Birnbaum et al (2020) which shows that users with mood disorders (bipolar disorder, major depressive disorder) and schizophrenia spectrum disorders use more swear words in their Facebook messages than healthy users
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