Tracking the Spread of Pollen on Social Media UsingPollen-Related Messages From Twitter: Retrospective Analysis

被引:0
|
作者
Perez-Perez, Martin [1 ,2 ,3 ]
Gonzalez, Maria Fernandez [4 ]
Rodriguez-Rajo, Francisco Javier [4 ]
Fdez-Riverola, Florentino [1 ,2 ,3 ]
机构
[1] Univ Vigo, CINBIO, Vigo, Spain
[2] Univ Vigo, Sch Comp Engn, Dept Comp Sci, Polytech Bldg,Univ Campus As Lagoas S-N, Orense 32004, Spain
[3] Galicia Hlth Res Inst, Sch Comp Engn, Galician Hlth Serv SERGAS UVIGO, Next Generat Comp Syst Grp, Orense, Spain
[4] Univ Vigo, Fac Sci, Dept Plant Biol & Soil Sci, Orense, Spain
关键词
pollen; respiratory allergies; large language model; LLM; knowledge reconstruction; text mining; CENTRALITY; ALLERGY; NETWORKS; SYMPTOMS; DISEASES; TWEET; REAL; TOOL;
D O I
10.2196/58309
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecastsindicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change. Objective: The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, there search sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing ourunderstanding of how pollen-related information spreads and its implications for public health awareness. Methods: Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency-inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant theme sand patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter. Results: The months between March and August had the highest volume of messages. The percentage of patient tweets appearedto increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is aglobal issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses. Conclusions: The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.
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页数:31
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