BACKGROUND
Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social
sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is
related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and
countries took actions to decrease the impact. South American countries have a different context about Economy, Health and
Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of
this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as
data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different
perspectives i.e. Economics, Social.
OBJECTIVE
Analyze the behaviour of South American Capitals in front of covid19 pandemics and show the helpfulness of Text Mining Approach for Infoveillance tasks.
METHODS
Text Mining process
RESULTS
- Argentina and Venezuela capitals are the biggest number of post during this period, opposite with Bolivia, Ecuador and Uruguay.
- Most relevant users are related to mass media like radio, television or newspapers.
- There is a general concern about covid19 but every country talks about different areas: Economics, Health, Environmental Impact.
CONCLUSIONS
Infoveillance based on Social Sensors with data coming from Twitter can help to understand the trends on the population of the
capitals. Besides, it is necessary to filter the posts for processing the text and get insights about frequency, top users, most
important terms. This data is useful to analyse the population from different approaches.
INTERNATIONAL REGISTERED REPORT
RR2-https://doi.org/10.1101/2020.04.06.20055749
The pandemic originated by coronavirus(covid19), name coined
by World Health Organization during the first month in 2020. Actually,
almost all the countries presented covid19 positive cases and govern-
ments are choosing different health policies to stop the infection and
many research groups are working on patients data to understand the
virus, at the same time scientists are looking for a vacuum to enhance
imnulogy system to tack covid19 virus. One of top countries with more
infections is Brazil, until August 11 had a total of 3,112,393 cases. Re-
search Foundation of Sao Paulo State(Fapesp) released a dataset, it was
an innovative in collaboration with hospitals(Einstein, Sirio-Libanes),
laboratory(Fleury) and Sao Paulo University to foster reseach on this
trend topic. The present paper presents an exploratory analysis of the
datasets, using a Data Mining Approach, and some inconsistencies are
found, i.e. NaN values, null references values for analytes, outliers on re-
sults of analytes, encoding issues. The results were cleaned datasets for
future studies, but at least a 20% of data were discarded because of non
numerical, null values and numbers out of reference range.
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