E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends.
E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends.
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DOI：
10.2196/publichealth.8641
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发表时间：
2017-12-20
影响因子：
8.5
通讯作者：
Unger JB
中科院分区：
文献类型：
--
作者：
Allem JP;Ferrara E;Uppu SP;Cruz TB;Unger JB
随着电子烟的使用迅速普及，来自在线社交系统（Twitter、Instagram、b谷歌网络搜索）的数据可用于捕捉和描述个人使用、感知和销售这种烟草产品的社会和环境背景。社交媒体数据可以作为一个庞大的焦点群体，人们在这里有机地讨论电子烟，没有研究人员的引子，没有工具偏见，几乎是实时的，成本很低。本研究记录了Twitter上与电子烟相关的讨论，描述了Twitter用户经常讨论电子烟的对话主题和地点，以确定电子烟教育活动的优先领域。此外，这项研究表明，在试图理解与公共健康相关的行为和态度时，区分社交机器人和人类用户的重要性。从2016年12月24日到2017年4月21日，Twitter上与电子烟相关的帖子（N= 6185153）被收集。从网络科学中提取的技术被用来通过描述Twitter网络中共同出现的标签（概念集群）来确定电子烟的讨论。文章和元数据用于描述美国电子烟相关讨论的地理位置。机器学习模型被用来区分反映真实人类用户态度和行为的Twitter帖子和社交机器人的帖子。比值比由2x2列联表计算，以检测标签是否因来源（社交机器人与人类用户）而变化，使用Fisher精确检验来确定统计显著性。在人类用户的标签语料库中发现的聚类包括行为（例如，#vaping）、吸电子烟身份（例如，#vapelife）和吸电子烟社区（例如，#vapenation）。其他类别包括产品（例如，#液体），双重烟草使用（例如，#水烟）和多种物质使用（例如，#大麻）。在社交机器人的主题标签语料库中发现的聚类包括健康（例如，#健康）、戒烟（例如，#戒烟）和新产品（例如，#ismog）。与人类用户相比，社交机器人发布有关戒烟和新产品的标签的可能性要大得多。推文的数量在大西洋中部（如宾夕法尼亚州、新泽西州、马里兰州和纽约州）最高，其次是西海岸和西南部（如加利福尼亚州、亚利桑那州和内华达州）。社交媒体数据可用于补充和扩大对包括烟草制品使用在内的健康行为的监测。公共卫生研究人员可以利用这些数据和方法来识别新产品或设备。此外，这项研究的结果表明，在试图理解态度和行为时，区分社交机器人和人类的Twitter帖子非常重要。社交机器人可能会被用来宣传电子烟有助于戒烟的观点，并在新产品进入市场时推广它们。
As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs. This study documents e-cigarette–related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health–related behaviors and attitudes. E-cigarette–related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette–related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance. Clusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada). Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace.