Method for Collecting Relevant Topics from Twitter supported by Big Data

• Autores: Silva, Jesús, Senior Naveda, Alexa, Gamboa Suarez, Ramiro, Hernández Palma, Hugo, Niebles Núẽz, William
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652145
• Enlace del recurso: https://doi.org/10.1088/1742-6596/1432/1/012094; http://hdl.handle.net/10757/652145
• Nivel de acceso: acceso abierto
• Materia: Big data; Sports; Virtual reality; Data generation; Microblogging; Time-periods; User profile; Social networking (online); https://purl.org/pe-repo/ocde/ford#5.02.00

There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.