Behavior of symptoms on twitter
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Fecha
2015
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CEUR Workshop Proceedings, 1613-0073, Vol.1478, 2nd Annual International Symposium on Information Management and Big Data, 2015 p. 83-84
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SIMBig
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Abstract
With the amount of data available on social networks, new methodologies for the analysis of information are needed. Some methods allow the users to combine different types of data in order to extract relevant information. In this context, the present paper shows the application of a model via a platform in order to group together
information generated by Twitter users, thus facilitating the detection of trends and data related to particular symptoms. In order to implement the model, an analyzing tool that uses the Levenshtein distance was developed, to determine exactly what is required to convert a text into the following texts: ’gripa’-”flu”, ”dolor de cabeza”-”headache”, ’dolor de estomago’- ”stomachache”, ’fiebre’-”fever” and ’tos’- ”cough” in the area of Bogota. Among the ´ information collected, identifiable patterns emerged for each one of the texts.
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Keywords
Information management, Social networking (online), Levenshtein distance