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1.
Stud Health Technol Inform ; 272: 5-8, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32604586

RESUMO

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.


Assuntos
Betacoronavirus , Cannabis , Infecções por Coronavirus , Pandemias , Pneumonia Viral , Mídias Sociais , Transtornos Relacionados ao Uso de Substâncias , Analgésicos Opioides , COVID-19 , Humanos , SARS-CoV-2 , Rede Social
2.
Stud Health Technol Inform ; 272: 24-27, 2020 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-32604591

RESUMO

We randomly extracted publicly available Tweets mentioning COVID-19 related terms (n=2,558,474 Tweets) from Tweet corpora collected daily using an API from Jan 21st to May 3rd, 2020. We applied a clustering algorithm to publicly available Tweets authored by African Americans (n=1,763) to detect topics and sentiment applying natural language processing (NLP). We visualized fifteen topics (four themes) using network diagrams (Newman modularity 0.74). Compared to the COVID-19 related Tweets authored by others, positive sentiments, cohesively encouraging online discussions (e.g., Black strong 27.1%, growing up Blacks 22.8%, support Black business 17.0%, how to build resilience 7.8%), and COVID-19 prevention behaviors (e.g., masks 4.7%, encouraging social distancing 9.4%) were uniquely observed in African American Twitter communities. Application of topic modeling techniques to streaming social media Twitter provides the foundation for research team insights regarding information and future virtual based intervention and social media based health disparity research for COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Pandemias , Pneumonia Viral , COVID-19 , Humanos , SARS-CoV-2 , Mídias Sociais
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