How Social Media Stigmatizes Substance Use: A Trend Analysis Examining a Politicized Visit to a Pediatric Hospital Unit

Abstract

Social media has become a ubiquitous platform for language directly impacting women with opioid use disorder (OUD) and children with neonatal abstinence syndrome (NAS). This study examines stigmatizing language applications related to OUD and NAS on Twitter during an event involving a political figure’s visit to a pediatric hospital unit. Analysis of N = 2,789 tweets across three stages of the event showed notable trends. Before the event, 56.07% of tweets focused on information sharing, while during and after the event, 22.64% and 20.69% of tweets were categorized as medically divisive and political. Chi-square analyses indicate significant differences in tweet categories, with the political category experiencing the most substantial increase during and after the event. Despite sharing information and opinions, stigmatizing language regarding NAS and OUD was pervasive. The researchers argue for more active involvement of healthcare professionals on social media to promote accurate information and advise patients on where to access supportive information and resources.

Keywords

Opioid Use Disorder (OUD)Neonatal Abstinence Syndrome (NAS)Social Media Twitter Stigma Language

Get full access to this article

View all access options for this article.

References

Adamic

L. A.

Glance

(2005). The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd international workshop on link discovery (pp. 36–43): ACM.

Akram

Kumar

(2017). A study on positive and negative effects of social media on society. International Journal on Computer Science and Engineering, 5(10), 351–354. https://doi.org/10.26438/ijcse/v5i10.351354

Asur

Huberman

B. A.

(2010). Predicting the future with social media. Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010, Toronto, ON, Canada, 31 August 2010 - 03 September.

Bachhuber

M. A.

Mcginty

E. E.

Kennedy-hendricks

Niederdeppe

Barry

C. L.

(2015). Messaging to increase public support for naloxone distribution policies in the United States: Results from a randomized survey experiment. PLoS One, 10(7), Article e1300500. https://doi.org/10.1371/journal.pone.0130050

Barberá

Jost

J. T.

Nagler

Tucker

J. A.

Bonneau

(2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. https://doi.org/10.1177/0956797615594620

Barnett

G. A.

(2011). Encyclopedia of social networks. Sage Publications.

Bian

Zhao

Salloum

R. G.

Guo

Wang

Prosperi

Zhang

Ramirez-Diaz

L. J.

Sun

(2017). Using social media data to understand the impact of promotional information on laypeople’s discussions: A case study of lynch syndrome. Journal of Medical Internet Research, 19(12), e414–e416. https://doi.org/10.2196/jmir.9266

Bimber

Davis

(2003). Campaigning online: The Internet in U.S. elections. Oxford University Press.

Bishop

(2019). Healthcare social media for consumer informatics. In Consumer informatics and digital health (pp. 61–86). Springer International Publishing. https://doi.org/10.1007/978-3-319-96906-0_4

10.

Burnap

Gibson

Sloan

Southern

Williams

(2016). 140 characters to victory? Using twitter to predict the UK 2015 general election. Electoral Studies, 41, 230–233. https://doi.org/10.1016/j.electstud.2015.11.017

11.

Center for Substance Abuse Treatment . (2004). Substance use disorders: A guide to the use of language. Center for Substance Abuse Treatment.

12.

Ceron

Curini

Iacus

S. M.

Porro

(2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358. https://doi.org/10.1177/1461444813480466

13.

Chenworth

Perrone

Love

J. S.

Graves

Hogg-Bremer

Sarker

(2021). Methadone and suboxone^® mentions on twitter: Thematic and sentiment analysis. Clinical Toxicology, 59(11), 982–991. https://doi.org/10.1080/15563650.2021.1893742

14.

Cinelli

De Francisci Morales

Galeazzi

Quattrociocchi

Starnini

(2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), Article e2023301118. https://doi.org/10.1073/pnas.2023301118

15.

Cleveland

L. M.

Gill

S. L.

(2013). “Try not to judge”: Mothers of substance exposed infants. MCN: The American Journal of Maternal/Child Nursing, 38(4), 200–205. https://doi.org/10.1097/NMC.0b013e31827816de

16.

Colleoni

Rozza

Arvidsson

(2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64(2), 317–332. https://doi.org/10.1111/jcom.12084

17.

Conover

M. D.

Ratkiewicz

Francisco

Gonçalves

Flammini

Menczer

(2011). Political polarization on Twitter. Proceedings of the International AAAI Conference on Weblogs and Social Media, 5(1), 89–96. https://doi.org/10.1609/icwsm.v5i1.14126

18.

Davis

(1999). The web of politics: The Internet’s impact on the American political system. Oxford University Press.

19.

Dekeseredy

Sedney

C. L.

Razzaq

Haggerty

Brownstein

H. H.

(2021). Tweeting stigma: An exploration of twitter discourse regarding medications used for both opioid use disorder and chronic pain. Journal of Drug Issues, 51(2), 340–357. https://doi.org/10.1177/0022042620986508

20.

Dubriwny

T. N.

(2008). Constructing breast cancer in the news: Betty Ford and the evolution of the breast cancer patient. Journal of Communication Inquiry, 33(2), 104–125. https://doi.org/10.1177/0196859908329090

21.

Edo-Osagie

De La Iglesia

Lake

Edeghere

(2020). A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine, 122, Article 103770. https://doi.org/10.1016/j.compbiomed.2020.103770

22.

Education Development Center . (2017). Words matter: How language choice can reduce stigma. Education Development Center.

23.

Evans

Twomey

Talan

(2011). Twitter as a public relations tool. Public Relations Journal, 5(1), 1–20.

24.

Fischer

Jagsch

Eder

Gombas

Etzersdorfer

Schmidl-Mohl

Schatten

Weninger

Aschauer

H. N.

(1999). Comparison of methadone and slow-release morphine maintenance in pregnant addicts. Addiction, 94(2), 231–239. https://doi.org/10.1046/j.1360-0443.1999.9422317.x

25.

Galston

W. A.

(2003). If political fragmentation is the problem, is the Internet the solution? In Anderson

D. M.

Cornfield

(Eds.), The civic web: Online politics and democratic values. Rowman & Littlefield.

26.

Gromping

(2014). Echo chambers: Partisan Facebook groups during the 2014 Thai election. Asia Pacific Media Educator, 24(1), 39–59. https://doi.org/10.1177/1326365x14539185

27.

Guess

A. M.

Lyons

B. A.

(2020). Misinformation, disinformation, and online propaganda. In Persily

Tucker

J. A.

(Eds.), Social media and democracy (pp. 2–33): Cambridge University Press.

28.

Healthcare Cost and Utilization Project . (2021). Neonatal abstinence syndrome (NAS) among newborn hospitalizations. Healthcare Cost and Utilization Project.

29.

Honeycutt

Herring

S. C.

(2009). Beyond microblogging: Conversation and collaboration via Twitter. In Paper presented at the 42nd Hawaii international conference on system sciences, Waikoloa, HI, USA, 05-08 January 2009.

30.

Hunt

(2017). Fake news’ named Collins dictionary’s official Word of the Year. The Independent.

31.

Hunt

R. W.

Tzioumi

Collins

Jeffery

H. E.

(2008). Adverse neurodevelopmental outcome of infants exposed to opiate in-utero. Early Human Development, 84(1), 29–35. https://doi.org/10.1016/j.earlhumdev.2007.01.013

32.

Hutto

Gilbert

(2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550

33.

Jay

(2022). Number of twitter users 2022/2023: Demographics, breakdowns & predictions- Financesonline.com. [online] Financesonline.com. Available at: [Accessed 1 July 2023].

34.

Jungherr

Jürgens

Schoen

(2011). Why the pirate party won the German election of 2009 or the trouble with predictions: A response to tumasjan, A., sprenger, T. O., Sander, P. G., & welpe, I. M. “predicting elections with twitter: What 140 characters reveal about political sentiment. Social Science Computer Review, 30(2), 229–234. https://doi.org/10.1177/0894439311404119

35.

Kelly

Saitz

Wakeman

(2016). Language, substance use disorders, and policy: The need to reach consensus on an “addiction-ary.”. Alcoholism Treatment Quarterly, 34(1), 116–123. https://doi.org/10.1080/07347324.2016.1113103

36.

Kelly

J. F.

Dow

S. J.

Westerhoff

(2010). Does our choice of substance related terms influence perceptions of treatment need? An empirical investigation with two commonly terms. Journal of Drug Issues, 40(4), 805–818. https://doi.org/10.1177/002204261004000403

37.

Kim

(2011). The contribution of social network sites to exposure to political difference: The relationships among SNSs, online political messaging, and exposure to cross-cutting perspectives. Computers in Human Behavior, 27(2), 971–977. https://doi.org/10.1016/j.chb.2010.12.001

38.

Kowalchuk

Mejia de

G. M.

Zoorob

(2018). Stigmatizing Language about neonatal abstinence syndrome. The Journal of the American Medical Association, 320(7), 722–723.

39.

McGinty

Pescosolido

Kennedy-Hendricks

Barry

C. L.

(2017). Communication strategies to counter stigma and improve mental illness and substance use disorder policy. Psychiatric Services, 69(2), 136–146. https://doi.org/10.1176/appi.ps.201700076

40.

Meel

Vishwakarma

(2020). Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Expert Systems with Applications, 153, Article 112986. https://doi.org/10.1016/j.eswa.2019.112986

41.

Mutz

D. C.

Martin

P. S.

(2001). Facilitating communication across lines of political difference: The role of mass media. American Political Science Review, 95(1), 97–114. https://doi.org/10.1017/s0003055401000223

42.

National Institute on Drug Abuse . (2021). Words matter - terms to use and avoid when talking about addiction. National Institute on Drug Abuse.

43.

Newman

M. E.

(2003). Mixing patterns in networks. Physical Review, 67(2 Pt 2), Article 026126. https://doi.org/10.1103/PhysRevE.67.026126

44.

Noveck

B. S.

(2000). Paradoxical partners: Electronic communication and electronic democracy. Democratization, 7(1), 18–35. https://doi.org/10.1080/13510340008403643

45.

O’Brien

Hunt

Jeffery

H. E.

(2004). Measurement of movement is an objective method to assist in assessment of opiate withdrawal in newborns. Archives of Disease in Childhood, 89(4), F305–F309. https://doi.org/10.1136/adc.2002.025270

46.

Office of National Drug Control Policy . (2017). Changing the Language of addiction. Office of National Drug Control Policy.

47.

Pal

Gonawela

(2017). Studying political communication on Twitter: The case for small data. Current Opinion in Behavioral Sciences, 18, 97–102. https://doi.org/10.1016/j.cobeha.2017.09.009

48.

Park

C. S.

(2013). Does twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641–1648. https://doi.org/10.1016/j.chb.2013.01.044

49.

Parmalee

J. H.

Bichard

S. L.

(2012). Politics and the Twitter revolution: How tweets influence the relationship between political leaders and the public. Lexington Books.

50.

Pew Research Center . (2021). Social media fact sheet. Pew Research Center.

51.

Prior

(2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. Cambridge University Press.

52.

Quattrociocchi

Scala

Sunstein

C. R.

(2016). Echo chambers on Facebook. SSRN. https://doi.org/10.2139/ssrn.2795110 (Accessed 19 August 2024).

53.

Radzikowski

Stefanidis

Jacobsen

K. H.

Croitoru

Crooks

Delamater

P. L.

(2016). The measles vaccination narrative in twitter: A quantitative analysis. JMIR Public Health Surveillance, 2(1), Article e1. https://doi.org/10.2196/publichealth.5059

54.

Ramo

D. E.

Meacham

M. C.

Kaur

Corpuz

E. S.

Prochaska

J. J.

Satre

D. D.

(2019). Development of a social media-based intervention targeting tobacco use and heavy episodic drinking in young adults. Addiction Science & Clinical Practice, 14(1), 14. https://doi.org/10.1186/s13722-019-0141-9

55.

R Core Team . (2020). R: a language and environment for statistical computing. R Foundation for Statistical Computing.

56.

Recovery Research Institute . (2022). Addictionary. https://www.recoveryanswers.org/addiction-ary

57.

Roose

(2021). What is QAnon, the viral pro-trump conspiracy theory? The New York Times.

58.

Rosen

(2019). Boston medical center employees to protest Melania Trump’s visit on Wednesday. The Boston Globe.

59.

Shannon

Chen

(2019). As first Lady visits Boston medical center, some protest outside the hospital. Boston University Radio.

60.

Shapiro

B. J.

Azari

Jones

H. E.

(2018). Stigmatizing Language about neonatal abstinence syndrome. JAMA, 320(7), 723. https://doi.org/10.1001/jama.2018.7820

61.

Short

J. L.

Shogun

C. J.

Owings

N. M.

(2005). The Influence of First Ladies on Mental Health Policy. White House Studies, 5(1), 65–76.

62.

Skoric

Poor

Achananuparp

(2012). Tweets and votes: A study of the 2011 Singapore general election. 45th Hawaii international conference on system sciences (HICSS), Maui HI, USA, 04-07 January 2012.

63.

Soroush

Deb

Sinan

(2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559

64.

Sprinklr . (2009). Definitions.Net. STANDS4 LLC.

65.

Starbird

Maddock

Orand

Achterman

Mason

R. M.

(2014). Rumors, false flags, and digital vigilantes: Misinformation on twitter after the 2013 Boston marathon bombing. IConference 2014 proceedings.

66.

Stone

(2015). Pregnant women and substance use: Fear, stigma, and barriers to care. Health & Justice, 3(1), 2–15. https://doi.org/10.1186/s40352-015-0015-5

67.

Sunstein

(2001). Republic.com. Princeton University Press.

68.

Van Bavel

J. J.

Harris

E. A.

Pärnamets

Rathje

Doell

K. C.

Tucker

J. A.

(2021). Political psychology in the digital (mis)information age: A model of news belief and sharing. Social Issues and Policy Review, 15(1), 84–113. https://doi.org/10.1111/sipr.12077

69.

Van Dijck

(2013). “You have one identity”: Performing the self on Facebook and LinkedIn. Media, Culture & Society, 35(2), 199–215. https://doi.org/10.1177/0163443712468605

70.

Wachman

(2021). BUSM CALM (cuddling Assists in lowering maternal and infant stress). Boston University School of Medicine. https://www.bumc.bu.edu/busm/student-affairs/student-life/service-learning/busm-calm-cuddling-assists-in-lowering-maternal-and-infant-stress/

71.

Webster

(2018). Open letter to journalists and editors about “Addicted Babies.”.

72.

Whiteman

V. E.

Salemi

J. L.

Mogos

M. F.

Cain

M. A.

Aliyu

M. H.

Salihu

H. M.

(2014). Maternal opioid drug use during pregnancy and its impact on perinatal morbidity, mortality, and the costs of medical care in the United States. Journal of Pregnancy, 2014(1), 906723–906728. https://doi.org/10.1155/2014/906723

73.

Wilhelm

(1998). Virtual sounding boards: How deliberative is online political discussion? Information, Communication & Society, 1(3), 313–338. https://doi.org/10.1080/13691189809358972

74.

W. W.

Zhang

(2018). Sentiment, richness, authority, and relevance model of information sharing during social crises—the case of #MH370 tweets. Computers in Human Behavior, 89, 199–206. https://doi.org/10.1016/j.chb.2018.07.041

75.

Zhang

Wheldon

Dunn

A. G.

Tao

Huo

Zhang

Prosperi

Guo

Bian

(2019). Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States. Journal of the American Medical Informatics Association, 27(2), 225–235. https://doi.org/10.1093/jamia/ocz191