Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day
| Autor: | Gianni Andreozzi, Marta Mosca, Valentina Lorenzoni, Giuseppe Turchetti, Salvatore Pirri |
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| Rok vydání: | 2020 |
| Předmět: |
Topic model
medicine.medical_specialty social media Health Toxicology and Mutagenesis media_common.quotation_subject topic modeling Internet privacy lcsh:Medicine 02 engineering and technology Article 03 medical and health sciences 0302 clinical medicine 020204 information systems Health care 0202 electrical engineering electronic engineering information engineering medicine Humans Lupus Erythematosus Systemic Social media Narrative 030212 general & internal medicine network analysis systemic lupus erythematosus (SLE) media_common business.industry Public health lcsh:R Public Health Environmental and Occupational Health Opinion leadership text analysis Network analysis Systemic lupus erythematosus (SLE) Text analysis Topic modeling Influencer marketing Feeling Public Health business Psychology |
| Zdroj: | International Journal of Environmental Research and Public Health International Journal of Environmental Research and Public Health, Vol 17, Iss 5440, p 5440 (2020) Volume 17 Issue 15 |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph17155440 |
| Popis: | Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text. |
| Databáze: | OpenAIRE |
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