JOURNAL ARTICLE

Algorithmic Doors to Community and the Trap of Visibility: TikTok for Harm Reduction Activism in the U.S. Overdose Crisis.

  • Published In: Contemporary Drug Problems, 2024, v. 51, n. 2. P. 67 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Schlosser, Allison; Subramanian, Roma; Kirkpatrick, Ciera E.; Butler, Annie; Boling, Kelli S.; Hample, Jessica; Habecker, Patrick; Jones, Valerie 3 of 3

Abstract

This article examines how individuals engaged in harm reduction activism use TikTok to promote naloxone—a medication that reverses opioid overdoses—and build supportive online communities amid the ongoing opioid overdose crisis in the United States. Drawing on interviews with 13 TikTok users who posted with the hashtags #naloxonesaveslives or #Narcansaveslives, the study explores how participants leverage TikTok's features (e.g., hashtags, duets, stitches, and algorithmic content delivery) as "doors" to foster education, solidarity, and misinformation correction about drug use and harm reduction. However, the research also highlights "traps" of visibility on TikTok, including stigma, harassment, burnout, mental distress, and digital silencing through content flagging or shadow banning, which complicate activists' efforts. The findings underscore the platform's dual role as a space for harm reduction community-building and a site of structural and social challenges, with implications for supporting digital harm reduction activism and addressing barriers to naloxone access.

Additional Information

  • Source:Contemporary Drug Problems. 2024/06, Vol. 51, Issue 2, p67
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2024
  • ISSN:0091-4509
  • DOI:10.1177/00914509241252031
  • Accession Number:177518675
  • Copyright Statement:Copyright of Contemporary Drug Problems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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