Framework for Social Media Big Data Quality Analysis

Al-Hajjar, Dua’a; Jaafar, Nouf; Al-Jadaan, Manal; Alnutaifi, Reem

doi:10.1007/978-3-319-10518-5_23

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…The proliferation of a rich family of big data applications (e.g., [2,3,8,73,70]) is a fortunate way to move from practice to theory, thus learning innovative big data aggregation and multidimensional analysis models directly from the in-field experiences with big data management. This paradigm, which has already shown to be successfully in other data-intensive research experiences, is more and more relevant for the special case of big data research.…”

Section: The Strength Of Applications: From Practice To Theorymentioning

confidence: 99%

“…The proposed methods are implemented on top of SciDB [10,71] a popular array database system designed to store and manipulate big array data. [70] proposes GiF, a real-life platform for supporting big data aggregation, analysis and mining of social media (e.g., [2,53]) in an integrated manner. Social media providers, such as Twitter, Facebook, Linkedin, and so forth, are real-life big data providers as here media are typically enormous in size, and their aggregation and analysis represents a very hard occurrence of the general problem we investigate in this paper.…”

Section: Big Data Aggregation and Multidimensional Analysis Techniquementioning

confidence: 99%

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Aggregation and multidimensional analysis of big data for large-scale scientific applications

Cuzzocrea

2015

Proceedings of the 27th International Conference on Scientific and Statistical Database Management

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Aggregation and multidimensional analysis are well-known powerful tools for extracting useful knowledge, shaped in a summarized manner, which are being successfully applied to the annoying problem of managing and mining big data produced by large-scale scientific applications. Indeed, in the context of big data analytics, aggregation approaches allow us to provide meaningful descriptions of these data, otherwise impossible for alternative data-intensive analysis tools. On the other hand, multidimensional analysis methodologies introduce fortunate metaphors that significantly empathize the knowledge discovery phase from such huge amounts of data. Following this main trend, several big data aggregation and multidimensional analysis approaches have been proposed recently. The goal of this paper is to (i) provide a comprehensive overview of state-of-the-art techniques and (ii) depict open research challenges and future directions adhering to the reference scientific field.

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Section: The Strength Of Applications: From Practice To Theorymentioning

confidence: 99%

Section: Big Data Aggregation and Multidimensional Analysis Techniquementioning

confidence: 99%

Aggregation and multidimensional analysis of big data for large-scale scientific applications

Cuzzocrea

2015

Proceedings of the 27th International Conference on Scientific and Statistical Database Management

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“…Finally, completeness is also important because the location of the major users and the socioeconomic backgrounds of the contributors frequently alter the distribution of user contributions [18]. According to Hajjar et al [19], there are some issues that reside in two important features in LinkedIn. For "skills and expertise" feature, the unstructured textual data like abbreviations and contextual data.…”

Section: Rq1 : What Are the Main Manifestations Of Data Quality Issue...mentioning

confidence: 99%

“…The quality and reliability of the videos was assessed according to the global quality score (GQS) and discern score. Hajjar et al [19] discussed the data quality issues related to data collected on social media. They choose for each social media platform, the techniques used for data capture, analysis and processing with the satisfied data quality model.…”

Section: Rq2: What Are the Most Important Dimensions And Metrics To A...mentioning

confidence: 99%

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Assessing the quality of social media data: a systematic literature review

Reda

Zellou²

2023

Bulletin EEI

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In recent years, social media have been at the heart of new communication technologies. They are no longer used only to facilitate interaction between family, friends, and professional relationships, but tend to become an increasingly used communication channel to address public opinion. Research involving data sources from social media are relatively a recent and expanding area of research, nevertheless, the literature remains limited regarding the complex issue of how to assess and ensure the quality of social media data significantly and adequately. Our goal in this study is to provide a clearer and deeper understanding and a comprehensive overview of the existing state of research pertaining to the assessment of data quality in social media context. We performed a systematic literature review (SLR) on the quality of social media data to collect, analyze, and discuss data on the accuracy and value of prior literature that has focused on this area, has addressed a variety of topics, and has been published between 2016 and 2021. We followed a predefined review process to cover all relevant research papers published during this period. Our results demonstrate and strengthen the significance and the importance of data quality especially in the context of social media.

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