Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

Abstract

Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine-than masculinegendered mainly young or middle-aged USbased adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues.

Modeling chronic pain experiences from online reports using the Reddit Reports of Chronic Pain dataset

“…Importantly, this is opposite to what was observed in the overall Reddit population 3 but aligned with chronic pain's prevalence in female-identi ed subjects. Although we cannot conclude that the number of reported binary gender data is su cient to support the small difference in reported female-and male-identi ed authors, indeed it is supported by other Reddit-based health studies that observed similar distributions [17]. Only 6.90% of RRCP authors were characterized regarding age.…”

“…We did not extract any other author information. To this end, we ran adapted versions of the public Sherlock algorithm and publicly available code used in a similar work [17], hereby called Glorianna.…”

“…Glorianna was set to extract age and binary gender. Notably, none of the algorithms extracts non-binary gender statements due to the lack of NLP techniques and data representation [17]. We used both tools because they complement each other in pattern matching rules.…”

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

“…However, with the rapid mood swings in BD and limited self-reports from patients, there is a significant gap in understanding the actual path of mood changes between the real world and the conventional clinical setting where clinicians can only see patients under limited conditions and rely on the subjective words of the patients [27]. Hence, using real-world data derived from patient reports at the nonclinical scene, such as social media, is helpful to understand better BD symptoms [31,48,63,78].…”

“…Given the significance of clinical understanding, two psychiatrists validate the annotated dataset with a pairwise annotator agreement of 0.77 and a groupwise agreement of 0.88. Unlike the existing datasets [22,31,68,70], the proposed dataset both includes (i) future suicidality of BD patients and (ii) a user's mood history that can be important features for diagnosing mood episodes [55] and future suicidality [32].…”