Behavior of symptoms on twitter

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 plat...

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Autores:
Salcedo, Dennis
Leon, Alejandro
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
eng
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/1588
Acceso en línea:
http://hdl.handle.net/20.500.12495/1588
Palabra clave:
Information management
Social networking (online)
Levenshtein distance
Redes sociales en línea
Big data
Procesamiento de la información
Rights
License
Acceso cerrado
Description
Summary: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.