Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns of opioid abuse at a national scale, understand opinions of the public, and provide insights to support prevention and treatment. Reddit is a community based social media with more reliable content curated by the community through voting. In this study, we collected and analyzed all opioid related discussions from January 2014 to October 2017, which contains 51,537 posts by 16,162 unique users. We analyzed the data to understand the psychological categories of the posts, and performed topic modeling to reveal the major topics of interest. We also characterized the extent of social support received from comments and scores by each post. Last, we analyzed statistically significant difference in the posts between anonymous and non-anonymous users.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371364PMC

Publication Analysis

Top Keywords

social media
12
social
4
media based
4
based analysis
4
opioid
4
analysis opioid
4
opioid epidemic
4
epidemic reddit
4
reddit opioid-abuse
4
opioid-abuse epidemic
4

Similar Publications

Objectives: There has been limited exploration into the nature and development of psychotic experiences (PEs) in Parkinson's disease (PD). We aimed to comprehensively assess the frequency, severity, and associated distress of paranoia and unusual sensory experiences (USEs) in PD, and to assess what variables are significantly associated with these experiences, focussing on psychological processes central to understanding PEs in non-PD groups.

Method: A questionnaire battery was completed by 369 individuals with PD with a mean age of 66 years and mean time since diagnosis of 5 years.

View Article and Find Full Text PDF

This study aimed to assess post-earthquake trauma levels in adults and explore the relationship between trauma, sleep disorders, dietary habits, and emotional eating. Conducted with 708 adults using snowball sampling, the study utilized the PROMIS Sleep Disturbance Scale, the Post-earthquake Trauma Level Determination Scale, and the Feeding Your Feelings: Emotional Eating Scale. Results revealed that factors such as gender, exposure to earthquake-related content on social media, time spent on social media before sleep, losing a loved one, and emotional eating tendencies significantly influenced trauma levels (Adj.

View Article and Find Full Text PDF

Background: As social media continue to grow as popular and convenient tools for acquiring and disseminating health information, the need to investigate its utilization by laypersons encountering common medical issues becomes increasingly essential.

Objectives: This study aimed to analyze the content posted in Facebook groups for Glucose-6-Phosphate Dehydrogenase (G6PD) deficiency and how these engage the members of the group.

Methods: This study employed an inductive content analysis of user-posted content in both public and private Facebook groups catering specifically to G6PD deficiency.

View Article and Find Full Text PDF

Background: Electronic cigarettes, introduced as a safer tobacco alternative, have unintentionally exposed millions of youths to nicotine and harmful chemicals. Adolescence, a key period for forming lifelong habits, has seen rising e-cigarette use, particularly in developing regions like Latin America, warranting thorough investigation.

Objective: To describe the prevalence and factors associated with e-cigarette use among adolescents in Latin America.

View Article and Find Full Text PDF

This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!