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Characterizing vaping posts on Instagram by using unsupervised machine learning.
International Journal of Medical Informatics ( IF 3.7 ) Pub Date : 2020-06-20 , DOI: 10.1016/j.ijmedinf.2020.104223
Vili Ketonen 1 , Aqdas Malik 1
Affiliation  

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.



中文翻译:

通过使用无监督机器学习来表征Instagram上的vaping帖子。

电子烟(电子烟)的使用已在全球范围内激增,尤其是在青少年中。社交媒体的日益普及,使得人们非常方便地访问和参与包括电子烟​​在内的多种物质的内容。使用Instagram应用程序编程界面检索了560,414个带有#vaping的图像帖子的全面数据集(从2019年6月1日至2019年10月31日共享)。深度神经网络用于提取图像特征,在这些图像特征上利用无监督的机器学习方法对图像进行聚类,然后对图像进行分类。进一步进行了相关元数据的描述性分析,以评估不同实体的影响以及不同类别中主题标签的使用。识别出与电子烟相关图像的七个不同类别。大部分图像(40.4%)描述了电子烟油,其次是电子烟(15.4%)。大约十分之一(9.9%)的数据集包含与人合影的照片。考虑到喜欢和评论的数量,描绘人物的图像获得了最高的参与度。与单个帐户相比,几乎在每个类别中,企业帐户平均共享更多职位。这些发现说明,在年轻人中普遍存在的社交平台上,电子烟的推广程度很高。监管机构应执行政策,限制在以青少年为目标的社交媒体中促销产品,并要求采取措施防止未成年人使用该内容。此外,

更新日期:2020-07-02
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