Analyzing the quality of Twitter data streams

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"There is a general belief that the quality of Twitter data streams is generally low and unpredictable, making, in some way, unreliable to take decisions based on such data. The work presented here addresses this problem from a Data Quality (DQ) perspective, adapting the traditional methods use...

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Autores principales: Arolfo, Franco, Cortés Rodriguez, Kevin, Vaisman, Alejandro Ariel
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2022
Materias:
CALIDAD DE DATOS
REDES SOCIALES
TWITTER
Acceso en línea:https://ri.itba.edu.ar/handle/123456789/3997
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3997
record_format dspace
spelling I32-R138-123456789-39972022-12-07T13:06:07Z Analyzing the quality of Twitter data streams Arolfo, Franco Cortés Rodriguez, Kevin Vaisman, Alejandro Ariel CALIDAD DE DATOS REDES SOCIALES TWITTER "There is a general belief that the quality of Twitter data streams is generally low and unpredictable, making, in some way, unreliable to take decisions based on such data. The work presented here addresses this problem from a Data Quality (DQ) perspective, adapting the traditional methods used in relational databases, based on quality dimensions and metrics, to capture the characteristics of Twitter data streams in particular, and of Big Data in a more general sense. Therefore, as a first contribution, this paper re-defines the classic DQ dimensions and metrics for the scenario under study. Second, the paper introduces a software tool that allows capturing Twitter data streams in real time, computing their DQ and displaying the results through a wide variety of graphics. As a third contribution of this paper, using the aforementioned machinery, a thorough analysis of the DQ of Twitter streams is performed, based on four dimensions: Readability, Completeness, Usefulness, and Trustworthiness. These dimensions are studied for several different cases, namely unfiltered data streams, data streams filtered using a collection of keywords, and classifying tweets referring to different topics, studying the DQ for each topic. Further, although it is well known that the number of geolocalized tweets is very low, the paper studies the DQ of tweets with respect to the place from where they are posted. Last but not least, the tool allows changing the weights of each quality dimension considered in the computation of the overall data quality of a tweet. This allows defining weights that fit different analysis contexts and/or different user profiles. Interestingly, this study reveals that the quality of Twitter streams is higher than what would have been expected." 2022-11-14T17:50:12Z 2022-11-14T17:50:12Z 2022 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 1572-9419 https://ri.itba.edu.ar/handle/123456789/3997 en info:eu-repo/semantics/altIdentifier/doi/10.1007/s10796-020-10072-x info:eu-repo/grantAgreement/ANPCyT/PICT/2017-1054/AR. Ciudad Autónoma de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic CALIDAD DE DATOS
REDES SOCIALES
TWITTER
spellingShingle CALIDAD DE DATOS
REDES SOCIALES
TWITTER
Arolfo, Franco
Cortés Rodriguez, Kevin
Vaisman, Alejandro Ariel
Analyzing the quality of Twitter data streams
topic_facet CALIDAD DE DATOS
REDES SOCIALES
TWITTER
description "There is a general belief that the quality of Twitter data streams is generally low and unpredictable, making, in some way, unreliable to take decisions based on such data. The work presented here addresses this problem from a Data Quality (DQ) perspective, adapting the traditional methods used in relational databases, based on quality dimensions and metrics, to capture the characteristics of Twitter data streams in particular, and of Big Data in a more general sense. Therefore, as a first contribution, this paper re-defines the classic DQ dimensions and metrics for the scenario under study. Second, the paper introduces a software tool that allows capturing Twitter data streams in real time, computing their DQ and displaying the results through a wide variety of graphics. As a third contribution of this paper, using the aforementioned machinery, a thorough analysis of the DQ of Twitter streams is performed, based on four dimensions: Readability, Completeness, Usefulness, and Trustworthiness. These dimensions are studied for several different cases, namely unfiltered data streams, data streams filtered using a collection of keywords, and classifying tweets referring to different topics, studying the DQ for each topic. Further, although it is well known that the number of geolocalized tweets is very low, the paper studies the DQ of tweets with respect to the place from where they are posted. Last but not least, the tool allows changing the weights of each quality dimension considered in the computation of the overall data quality of a tweet. This allows defining weights that fit different analysis contexts and/or different user profiles. Interestingly, this study reveals that the quality of Twitter streams is higher than what would have been expected."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Arolfo, Franco
Cortés Rodriguez, Kevin
Vaisman, Alejandro Ariel
author_facet Arolfo, Franco
Cortés Rodriguez, Kevin
Vaisman, Alejandro Ariel
author_sort Arolfo, Franco
title Analyzing the quality of Twitter data streams
title_short Analyzing the quality of Twitter data streams
title_full Analyzing the quality of Twitter data streams
title_fullStr Analyzing the quality of Twitter data streams
title_full_unstemmed Analyzing the quality of Twitter data streams
title_sort analyzing the quality of twitter data streams
publishDate 2022
url https://ri.itba.edu.ar/handle/123456789/3997
work_keys_str_mv AT arolfofranco analyzingthequalityoftwitterdatastreams
AT cortesrodriguezkevin analyzingthequalityoftwitterdatastreams
AT vaismanalejandroariel analyzingthequalityoftwitterdatastreams
_version_ 1765660837830197248

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