Analyzing the Quality of Twitter Data Streams
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Analyzing the Quality of Twitter Data Streams ´ Rodriguez1 · Alejandro Vaisman1 Franco Arolfo1 · Kevin Cortes Accepted: 28 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Data quality · Social networks · Twitter · Big data
1 Introduction and Motivation The relevance of Big Data has been acknowledged by researchers and practitioners even before the concept became widely popular through media coverage (The Economist. Data 2008). Although there is no precise and formal definition, it is accepted that Big Data refers to huge volumes of heterogeneous data ingested at a speed that cannot be handled by traditional database systems tools, Alejandro Vaisman
[email protected] Franco Arolfo [email protected] Kevin Cort´es Rodriguez [email protected] 1
Department of Information Engineering, Instituto Tecnol´ogico de Buenos Aires Lavard´en 315, C1437FBG, Ciudad Aut´onoma de Buenos Aires, Argentina
and characterized by the well-known “4 V’s” (volume, variety, velocity, and veracity). That means, not only the data volume is relevant, but also the different kinds of structured, semi-structured and unstructured data, the speed at which data arrives (e.g., real time, near real time), and the reliability and usefu
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