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. 2018 Dec 5:2018:867-876.
eCollection 2018.

Social Media Based Analysis of Opioid Epidemic Using Reddit

Sheetal Pandrekar  1 , Xin Chen  1 , Gaurav Gopalkrishna  1 , Avi Srivastava  1 , Mary Saltz  1 , Joel Saltz  1 , Fusheng Wang  1
Affiliations

Social Media Based Analysis of Opioid Epidemic Using Reddit

Sheetal Pandrekar et al. AMIA Annu Symp Proc. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Plot of RPC against the number of topics
Figure 2.
Figure 2.
Average time elapsed between two consecutive posts
Figure 3.
Figure 3.
Average time elapsed until first comment on a post
Figure 4.
Figure 4.
Mentions of different opioid categories over time.
Figure 5.
Figure 5.
Major psychological categories observed over time
Figure 6.
Figure 6.
Word clouds of the major topics of discussions related to opioid abuse
Figure 7.
Figure 7.
Percentage of posts per topic type
Figure 8.
Figure 8.
Comparison of comments and score between anonymous and non-anonymous users
See this image and copyright information in PMC

References

    1. Prevention CoDCa. Opioid Data Analysis. 2018. Jul 6, https://www.cdc.gov/drugoverdose/data/analysis.html.
    1. Chen X, Wang Y, Yu X, et al. Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State. 2017. arXivpreprint arXiv:171105779. - PMC - PubMed
    1. Lingeman JM, Wang P, Becker W, Yu H. Detecting Opioid-Related Aberrant Behavior using Natural Language Processing. AMIA. 2017. - PMC - PubMed
    1. A Che Z, Sauver JLS, A Liu H, A Liu Y. Deep Learning Solutions for Classifying Patients on Opioid Use. AMIA. 2017. - PMC - PubMed
    1. Ginn SK, Clark E. The medical profession and stigma against people who use drugs. The British Journal of Psychiatry. 2017;211(6):400–400. - PubMed

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