Characterizing vaping posts on instagram by using unsupervised machine learning

Vili Ketonen, Aqdas Malik*

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

34 Sitaatiot (Scopus)
243 Lataukset (Pure)


Electronic cigarettes (e-cigarettes) usage has surged substantially across the globe, particularly among adolescents and young adults. The ever-increasing prevalence of social media makes it highly convenient to access and engage with content on numerous substances, including e-cigarettes. A comprehensive dataset of 560,414 image posts with a mention of #vaping (shared from 1 June 2019 to 31 October 2019) was retrieved by using the Instagram application-programming interface. Deep neural networks were used to extract image features on which unsupervised machine-learning methods were leveraged to cluster and subsequently categorize the images. Descriptive analysis of associated metadata was further conducted to assess the influence of different entities and the use of hashtags within different categories. Seven distinct categories of vaping related images were identified. A majority of the images (40.4 %) depicted e-liquids, followed by e-cigarettes (15.4 %). Around one-tenth (9.9 %) of the dataset consisted of photos with person(s). Considering the number of likes and comments, images portraying person(s) gained the highest engagement. In almost every category, business accounts shared more posts on average compared to the individual accounts. The findings illustrate the high degree of e-cigarettes promotion on a social platform prevalent among youth. Regulatory authorities should enforce policies to restrict product promotion in youth-targeted social media, as well as require measures to prevent underage users' access to this content. Furthermore, a stronger presence of anti-tobacco portrayals on Instagram by public health agencies and anti-tobacco campaigners is needed.

JulkaisuInternational Journal of Medical Informatics
DOI - pysyväislinkit
TilaJulkaistu - syysk. 2020
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä


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