Yoon Sunmoo, Odlum Michelle, Broadwell Peter, Davis Nicole, Cho Hwayoug, Deng Nanyi, Patrao Maria, Schauer Deborah, Bales Michael E, Alcantara Carmela
Columbia University Medical Center.
School of Nursing, Columbia University.
Stud Health Technol Inform. 2020 Jun 26;272:5-8. doi: 10.3233/SHTI200479.
We applied social network analysis (SNA) to Tweets mentioning cannabis or opioid-related terms to publicly available COVID-19 related Tweets collected from Jan 21st to May 3rd, 2020 (n= 2,558,474 Tweets). We randomly extracted 16,154 Tweets mentioning cannabis and 4,670 Tweets mentioning opioids from the COVID-19 Tweet corpora for our analysis. The cannabis related Tweets created by 6,144 users were disseminated to 280,042,783 users and retweeted 11 times the number of original messages while opioid-related Tweets created by 3,412 users were disseminated to smaller number of users. The opioids Twitter network showed more cohesive online group activities and a cleaner online environment with less disinformation. The cannabis Twitter network showed a less desirable online environment with more disinformation (false information to mislead the public) and stakeholders lacking strong science knowledge. Application of SNA to Tweets provides insights for future online-based drug abuse research during the outbreak.
我们将社交网络分析（SNA）应用于提及大麻或阿片类药物相关术语的推文，这些推文来自于2020年1月21日至5月3日收集的公开可用的与新冠疫情相关的推文（共2,558,474条推文）。我们从新冠疫情推文语料库中随机提取了16,154条提及大麻的推文和4,670条提及阿片类药物的推文用于分析。由6,144名用户发布的与大麻相关的推文被传播给了280,042,783名用户，转发次数是原始推文的11倍，而由3,412名用户发布的与阿片类药物相关的推文传播给的用户数量较少。阿片类药物的推特网络显示出更具凝聚力的在线群体活动以及更清洁的在线环境，虚假信息较少。大麻的推特网络则显示出一个不太理想的在线环境，虚假信息（误导公众的错误信息）更多，且利益相关者缺乏扎实的科学知识。将社交网络分析应用于推文为疫情期间未来基于网络的药物滥用研究提供了见解。
