An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis.
An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis.
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YouTube上与电子烟相关的搜索项目的探索：网络分析。
DOI：
10.2196/30679
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发表时间：
2022-01-27
影响因子：
7.4
通讯作者：
Kong G
中科院分区：
文献类型：
--
作者：
Dashtian H;Murthy D;Kong G
青少年使用电子烟的比例很高，部分原因可能是YouTube等社交媒体上支持电子烟的内容。YouTube也是了解电子烟使用、趋势、营销和电子烟用户看法的宝贵资源。然而，对于相似的电子烟相关搜索项如何导致相似或相对互斥的搜索结果，人们缺乏理解。本研究采用新颖的方法来评估电子烟相关搜索项与结果之间的关系。本研究的目的是应用网络建模和基于规则的分类来表征YouTube上与电子烟相关的搜索项之间的关系，并衡量每个搜索项作为YouTube上电子烟信息网络的一部分的重要性。我们使用16个虚构的YouTube配置文件从18个与电子烟相关的关键字中检索4201个不同的视频。我们使用网络建模来表示搜索项之间的关系。此外，我们还开发了一种基于规则的视频分类方法。我们使用中间中心性（BC）和节点（即搜索项）之间的相关性来帮助我们获得信息网络底层结构的知识。通过将搜索项和视频建模为一个网络，我们观察到广泛的搜索项（如e- cigg）与其他搜索项的连接最多，而特定的搜索项（如cigalike）的连接最少。搜索词相似的项目（例如，vape和vaping）和搜索意义相似的项目（例如，电子液体和电子果汁）产生了高度的连通性。我们还发现每个节点平均有18个（SD 34.8）连接（常见视频）。BC表明，电子烟和vaping等一般搜索项在网络中具有很高的重要性（BC=0.00836）。我们基于规则的分类将视频分为四类：电子烟设备（34%-57%）、大麻雾化（16%-28%）、电子烟液体（14%-37%）和其他（8%-22%）。我们的研究结果表明，YouTube上的搜索项具有独特的关系，其强度和重要性各不相同。我们的方法不仅可以用来成功地识别重要的、重叠的和独特的电子烟相关搜索项，还可以帮助确定哪些搜索项更有可能成为电子烟相关内容的门户。
e-Cigarette use among youth is high, which may be due in part to pro–e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette–related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette–related search items and results. The aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette–related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube. We used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network. By modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34%-57%), cannabis vaping (16%-28%), e-liquid (14%-37%), and other (8%-22%). Our findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette–related search items but also help determine which search items are more likely to act as a gateway to e-cigarette–related content.
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DOI：
10.1016/j.ijmedinf.2020.104175
发表时间：
2020-08-01
影响因子：
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作者：
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通讯作者：
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