IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v70y2022ics0160791x2200149x.html
   My bibliography  Save this article

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

Author

Listed:
  • Malik, Aqdas
  • Berggren, Walter
  • Al-Busaidi, Adil S.

Abstract

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.

Suggested Citation

  • Malik, Aqdas & Berggren, Walter & Al-Busaidi, Adil S., 2022. "Instagram as a research tool for examining tobacco-related content: A methodological review," Technology in Society, Elsevier, vol. 70(C).
  • Handle: RePEc:eee:teinso:v:70:y:2022:i:c:s0160791x2200149x
    DOI: 10.1016/j.techsoc.2022.102008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X2200149X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2022.102008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Al-Razgan, Muna & Alrowily, Asma & Al-Matham, Rawan N. & Alghamdi, Khulood M. & Shaabi, Maha & Alssum, Lama, 2021. "Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women," Technology in Society, Elsevier, vol. 65(C).
    2. Sinnenberg, L. & Buttenheim, A.M. & Padrez, K. & Mancheno, C. & Ungar, L. & Merchant, R.M., 2017. "Twitter as a tool for health research: A systematic review," American Journal of Public Health, American Public Health Association, vol. 107(1), pages 1-8.
    3. repec:aph:ajpbhl:10.2105/ajph.2016.303512_4 is not listed on IDEAS
    4. Xing, Yunfei & Wang, Xiwei & Qiu, Chengcheng & Li, Yueqi & He, Wu, 2022. "Research on opinion polarization by big data analytics capabilities in online social networks," Technology in Society, Elsevier, vol. 68(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Camilleri, Mark Anthony & Kozak, Metin, 2022. "Interactive engagement through travel and tourism social media groups: A social facilitation theory perspective," Technology in Society, Elsevier, vol. 71(C).
    2. Jonine Jancey & Tama Leaver & Katharina Wolf & Becky Freeman & Kevin Chai & Stella Bialous & Marilyn Bromberg & Phoebe Adams & Meghan Mcleod & Renee N. Carey & Kahlia McCausland, 2023. "Promotion of E-Cigarettes on TikTok and Regulatory Considerations," IJERPH, MDPI, vol. 20(10), pages 1-10, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santoveña-Casal, Sonia & Pérez, Ma Dolores Fernández, 2022. "Relevance of E-Participation in the state health campaign in Spain: #EstoNoEsUnJuego / #ThisIsNotAGame," Technology in Society, Elsevier, vol. 68(C).
    2. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    3. Xing, Yunfei & Zhang, Justin Zuopeng & Teng, Guangqing & Zhou, Xiaotang, 2024. "Voices in the digital storm: Unraveling online polarization with ChatGPT," Technology in Society, Elsevier, vol. 77(C).
    4. Einav, Gali & Allen, Ofir & Gur, Tamar & Maaravi, Yossi & Ravner, Daniel, 2022. "Bursting filter bubbles in a digital age: Opening minds and reducing opinion polarization through digital platforms," Technology in Society, Elsevier, vol. 71(C).
    5. Muhammad Imran & Umair Qazi & Ferda Ofli, 2022. "TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels," Data, MDPI, vol. 7(1), pages 1-27, January.
    6. Isaac Chun-Hai Fung & Jingjing Yin & Keisha D. Pressley & Carmen H. Duke & Chen Mo & Hai Liang & King-Wa Fu & Zion Tsz Ho Tse & Su-I Hou, 2019. "Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014," Data, MDPI, vol. 4(2), pages 1-12, June.
    7. Nason Maani Hessari & May CI van Schalkwyk & Sian Thomas & Mark Petticrew, 2019. "Alcohol Industry CSR Organisations: What Can Their Twitter Activity Tell Us about Their Independence and Their Priorities? A Comparative Analysis," IJERPH, MDPI, vol. 16(5), pages 1-12, March.
    8. Xu, Yong & Yuan, Ling & Khalfaoui, Rabeh & Radulescu, Magdalena & Mallek, Sabrine & Zhao, Xin, 2023. "Making technological innovation greener: Does firm digital transformation work?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    9. Hanan S. AlEssa & Christopher M. Durugbo, 2022. "Understanding innovative work behaviour of women in service firms," Service Business, Springer;Pan-Pacific Business Association, vol. 16(4), pages 825-862, December.
    10. Sonja I. Garske & Suzanne Elayan & Martin Sykora & Tamar Edry & Linus B. Grabenhenrich & Sandro Galea & Sarah R. Lowe & Oliver Gruebner, 2021. "Space-Time Dependence of Emotions on Twitter after a Natural Disaster," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    11. Alshawawreh, Ali Ra’Ed & Liébana-Cabanillas, Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of big data analytics on telecom companies' competitive advantage," Technology in Society, Elsevier, vol. 76(C).
    12. Diane Ezeh Aruah & Yvonne Henshaw & Kim Walsh-Childers, 2023. "Tweets That Matter: Exploring the Solutions to Maternal Mortality in the United States Discussed by Advocacy Organizations on Twitter," IJERPH, MDPI, vol. 20(9), pages 1-14, April.
    13. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
    14. Wu, Yue & Li, Wenjia & Li, Yixiao & Chen, Qi & Liu, Mingyu & Li, Yuehui, 2024. "Alleviating negative group polarization with the aid of social bots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 644(C).
    15. Meng Hsiu Tsai & Yingfeng Wang, 2021. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    16. Gabrielle Turner-McGrievy & Amir Karami & Courtney Monroe & Heather M. Brandt, 2020. "Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1035-1049, August.
    17. Camilleri, Mark Anthony & Kozak, Metin, 2022. "Interactive engagement through travel and tourism social media groups: A social facilitation theory perspective," Technology in Society, Elsevier, vol. 71(C).
    18. Huarng, Kun-Huang & Lee, Cheng-Fang & Yu, Tiffany Hui-Kuang, 2023. "Case study of a healthcare virtual community model," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    19. Jairo León-Quismondo, 2023. "Social Sensing and Individual Brands in Sports: Lessons Learned from English-Language Reactions on Twitter to Pau Gasol’s Retirement Announcement," IJERPH, MDPI, vol. 20(2), pages 1-13, January.
    20. Ruth G. Abebe & Schwab Bakombo & Anne T. M. Konkle, 2023. "Understanding the Response of Canadians to the COVID-19 Pandemic Using the Kübler-Ross Model: Twitter Data Analysis," IJERPH, MDPI, vol. 20(4), pages 1-13, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:70:y:2022:i:c:s0160791x2200149x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.