Advanced search
Publication Cover
Clinical Toxicology Volume 59, 2021 - Issue 11
Submit an article Journal homepage
3,859
Views
7
CrossRef citations to date
0
Altmetric
Basic Research

Methadone and suboxone® mentions on twitter: thematic and sentiment analysis

Megan Chenwortha Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USACorrespondence[email protected]
View further author information
,
Jeanmarie Perroneb Department of Emergency Medicine, Center for Addiction Medicine and Policy, University of Pennsylvania, Philadelphia, PA, USAView further author information
,
Jennifer S. Lovec Department of Emergency Medicine, Oregon Health and Science University, Portland, OR, USAView further author information
,
Rachel Gravesb Department of Emergency Medicine, Center for Addiction Medicine and Policy, University of Pennsylvania, Philadelphia, PA, USAView further author information
,
Whitney Hogg-Bremerd Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USAView further author information
&
Abeed Sarkerd Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USAView further author information
Pages 982-991 | Received 11 Nov 2020, Accepted 17 Feb 2021, Published online: 06 Apr 2021

References

  • Mancher M, Leshner AI. National Academies of Sciences, Engineering, and Medicine. Medications for opioid use disorder save lives. Washington (DC): National Academies Press; 2019.
  • Hser YI, Evans E, Huang D, et al. Long-term outcomes after randomization to buprenorphine/naloxone versus methadone in a multi-site trial. Addiction. 2016;111:695–705.
  • Lemon LS, Caritis SN, Venkataramanan R, et al. Methadone versus buprenorphine for opioid use dependence and risk of neonatal abstinence syndrome. Epidemiology. 2018;29:261–268.
  • Strayer RJ, Hawk K, Hayes BD, et al. Management of opioid use disorder in the emergency department: a white paper prepared for the American Academy of Emergency Medicine. J Emerg Med. 2020;58:522–546.
  • Randall-Kosich O, Andraka-Christou B, Totaram R, et al. Comparing reasons for starting and stopping methadone, buprenorphine, and naltrexone treatment among a sample of white individuals with opioid use disorder. J Addict Med. 2019;14:e44–e52.
  • Aslam S. Twitter by the Numbers 2020. Stats, Demographics & Fun Facts. 2020. Available from: https://www.omnicoreagency.com/twitter-statistics/
  • Chary M, Genes N, Giraud-Carrier C, et al. Epidemiology from Tweets: estimating misuse of prescription opioids in the USA from social media. J Med Toxicol. 2017;13:278–286.
  • Tufts C, Polsky D, Volpp KG, et al. Characterizing Tweet volume and content about common health conditions across Pennsylvania: retrospective analysis. JMIR Public Health Surveill. 2018;4:e10834.
  • Graves RL, Tufts C, Meisel ZF, et al. Opioid discussion in the Twittersphere. Subst Use Misuse. 2018;53:2132–2139.
  • Sarker A, Gonzalez-Hernandez G, Ruan Y, et al. Machine learning and natural language processing for geolocation-centric monitoring and characterization of opioid-related social media chatter. JAMA Netw Open. 2019;2:e1914672.
  • Sarker A, Gonzalez-Hernandez G. An unsupervised and customizable misspelling generator for mining noisy health-related text sources. J Biomed Inform. 2018;88:98–107.
  • Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101.
  • Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.
  • Blei DM, Ng AY, Edu JB. Latent Dirichlet Allocation Michael I. Jordan. 2003;3.
  • Porter MF. An algorithm for suffix stripping. Program. 1980;14:130–137.
  • TextBlob: simplified text processing – TextBlob 0.16.0 documentation. [accessed 2020 Oct 26]. Was 23. Available from: https://textblob.readthedocs.io/en/dev/
  • Hasan A, Moin S, Karim A, et al. Machine learning-based sentiment analysis for twitter accounts. MCA. 2018;23:11.
  • Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37:360–363.
  • Berk J. To help providers fight the opioid epidemic, “X The X Waiver”. Health Affairs Blog. 2019.
  • Kennedy-Hendricks A, Busch SH, McGinty EE, et al. Primary care physicians’ perspectives on the prescription opioid epidemic. Drug Alcohol Depend. 2016;165:61–70.
  • Im DD, Chary A, Condella AL, et al. Emergency department clinicians’ attitudes toward opioid use disorder and emergency department-initiated buprenorphine treatment: a mixed-methods study. West J Emerg Med. 2020;21:261–271.
  • Martin A, Baugh J, Chavez T, et al. Clinician experience of nudges to increase ED OUD treatment. Am J Emerg Med. 2020;38:2241–2242.
  • Martin A, Kunzler N, Nakagawa J, et al. Get waivered: a resident-driven campaign to address the opioid overdose crisis. Ann Emerg Med. 2019;74:691–696.
  • Martin A, Mitchell A, Wakeman S, et al. Emergency department treatment of opioid addiction: an opportunity to lead. Acad Emerg Med. 2018;25:601–604.
  • Tofighi B, El Shahawy O, Segoshi A, et al. Assessing perceptions about medications for opioid use disorder and Naloxone on Twitter. J Addict Dis. 2020;39:37–45.
  • Yarborough BJH, Stumbo SP, McCarty D, et al. Methadone, buprenorphine and preferences for opioid agonist treatment: a qualitative analysis. Drug Alcohol Depend. 2016;160:112–118.
  • Brown SE, Altice FL. Self-management of buprenorphine/naloxone among online discussion board users. Subst Use Misuse. 2014;49:1017–1024.
  • Perrin A, Anderson M. Social media usage in the U.S. in 2019. Pew Res Cent. 2019. Available from: https://www.pewresearch.org/fact-tank/2019/04/10/share-of-u-s-adults-using-social-media-including-facebook-is-mostly-unchanged-since-2018/. Archived at: https://www.webcitation.org/78C8HLwUK
  • Farhadloo M, Winneg K, Chan MPS, et al. Associations of topics of discussion on Twitter with survey measures of attitudes, knowledge, and behaviors related to zika: probabilistic study in the United States. JMIR Public Health Surveill. 2018;4:e16.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.