BACKGROUND: Technological advancements, such as patient-centered smartphone applications, have enabled to support self-management of the disease. Further, the accessibility to health information through the Internet has grown tremendously. This article aimed to investigate how big data can be useful to assess the impact of a celebrity's rheumatic disease on the public opinion. METHODS: Variable tools and statistical/computational approaches have been used, including massive data mining of Google Trends, Wikipedia, Twitter, and big data analytics. These tools were mined using an in-house script, which facilitated the process of data collection, parsing, handling, processing, and normalization. RESULTS: From Google Trends, the temporal correlation between "Anna Marchesini" and rheumatoid arthritis (RA) queries resulted 0.66 before Anna Marchesini's death and 0.90 after Anna Marchesini's death. The geospatial correlation between "Anna Marchesini" and RA queries resulted 0.45 before Anna Marchesini's death and 0.52 after Anna Marchesini's death. From Wikitrends, after Anna Marchesini's death, the number of accesses to Wikipedia page for RA has increased 5770%. From Twitter, 1979 tweets have been retrieved. Numbers of likes, retweets, and hashtags have increased throughout time. CONCLUSIONS: Novel data streams and big data analytics are effective to assess the impact of a disease in a famous person on the laypeople.
BACKGROUND: Technological advancements, such as patient-centered smartphone applications, have enabled to support self-management of the disease. Further, the accessibility to health information through the Internet has grown tremendously. This article aimed to investigate how big data can be useful to assess the impact of a celebrity's rheumatic disease on the public opinion. METHODS: Variable tools and statistical/computational approaches have been used, including massive data mining of Google Trends, Wikipedia, Twitter, and big data analytics. These tools were mined using an in-house script, which facilitated the process of data collection, parsing, handling, processing, and normalization. RESULTS: From Google Trends, the temporal correlation between "Anna Marchesini" and rheumatoid arthritis (RA) queries resulted 0.66 before Anna Marchesini's death and 0.90 after Anna Marchesini's death. The geospatial correlation between "Anna Marchesini" and RA queries resulted 0.45 before Anna Marchesini's death and 0.52 after Anna Marchesini's death. From Wikitrends, after Anna Marchesini's death, the number of accesses to Wikipedia page for RA has increased 5770%. From Twitter, 1979 tweets have been retrieved. Numbers of likes, retweets, and hashtags have increased throughout time. CONCLUSIONS: Novel data streams and big data analytics are effective to assess the impact of a disease in a famous person on the laypeople.
Authors: Jacopo Ciaffi; Riccardo Meliconi; Maria Paola Landini; Luana Mancarella; Veronica Brusi; Cesare Faldini; Francesco Ursini Journal: Int J Environ Res Public Health Date: 2021-02-01 Impact factor: 3.390