Instagram as a research tool for examining tobacco-related content: A methodological review: A methodological review

Aqdas Malik*, Walter Berggren, Adil S. Al-Busaidi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Social media is rife with modifiable risky health behaviors and substance use topics, pre-cursors to peer-influence and social acceptability, which are drivers of behavioral change. With over a billion active users, Instagram is one of the leading social media platforms across the globe, especially among adolescents and young adults for obtaining, sharing, and promoting tobacco-related content. With an aim to assess the current landscape and inform future research, our review summarizes and analyzes the methodological techniques and approaches used for categorically coding Instagram-based data about tobacco. By using relevant keywords, a literature search was performed in June 2021 within three databases – Web of Science, Scopus, and PubMed – identifying 304 articles. PRISMA (Preferred Reporting Items for Systematics Reviews and Meta-Analyses) guidelines were adopted to direct further analysis and reporting. Inclusion and exclusion criteria were used by two reviewers to systematically assess the eligibility of studies resulting in 27 studies. Key characteristics (product studied, focus of the study, details about data collection, and coding and coded categories) from each study were extracted and analyzed in detail. E-cigarettes were the most frequently investigated tobacco product followed by the hookah/water pipe, cigars/cigarillos, betel nut, and Heated Tobacco Products (HTP). As the data source, Netlytic and Instagram's API/website were commonly used. The coding methods broadly encompass human coding and machine-learning techniques. As a rich and organic source, Instagram-based data is valuable for the surveillance of various forms of tobacco as well as substance use. Open and simpler data collection tools are needed as collecting Instagram data has become challenging. Blending hand-coding with machine-learning techniques may advance future research to classify broader representation and understand nuanced behaviors around tobacco portrayals on Instagram.

Original languageEnglish
Article number102008
Pages (from-to)102008
Number of pages1
JournalTechnology in Society
Publication statusPublished - Aug 1 2022


  • Instagram
  • Nicotine
  • Photos
  • Social media
  • Social networking
  • Tobacco

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Business and International Management
  • Education
  • Sociology and Political Science

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