@conference {Reda2022,
	title = {SMDQM-Social Media Data Quality Assessment Model},
	booktitle = {2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Social media has taken a dominant place in people{\textquoteright}s lives since it become more than just a mode of communication between individuals. They regularly share their daily activities, experiences, and exchange interests and opinions on social media sites, leading to a flood of information that can be used by consumers. However, the reduced posting barriers, no operational costs and easy-to-use interactive interfaces have combined to create a variety of data quality issues in social media, making it difficult to obtain relevant, timely and accurate data. Given the increasing use of social media by users to obtain news and information first hand, they are required to filter a significant volume of data to reach the most satisfying to their request, therefore we can say that the process is more qualitative than quantitative. This paper proposes a new classification of data quality dimensions towards a social media data quality model called SMDQM(Social Media Data Quality Model), for this the quality of social media data is discussed, then we present the steps involved to create our model SMDQM. This attempt will be established by considering and discussing a state of the art in the field of social media and data quality. {\textcopyright} 2022 IEEE.},
	keywords = {Assessment, Daily activity, data quality, Data quality assessment, Data quality models, Data reduction, Interactive interfaces, Quality assessment model, Quality modeling, social media, Social media datum, Social networking (online)},
	doi = {10.1109/IRASET52964.2022.9738330},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127964567\&doi=10.1109\%2fIRASET52964.2022.9738330\&partnerID=40\&md5=566d4361e0b4742bd3c69530f2eaf99f},
	author = {Reda, O. and Zellou, A.}
}
@conference {Reda202091,
	title = {Towards a data quality assessment in big data},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 4},
	pages = {91-96},
	abstract = {In recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyone wonder what the results of these analyses will really be like. However, there is a very complex process involved in the identification of new, valid, potentially useful and meaningful data from a large data collection and various information systems, and is critically dependent on a number of measures to be developed to ensure data quality. To this end, the main objective of this paper is to introduce a general study on data quality related with big data, by providing what other researchers came up with on that subject. The paper will be finalized by a comparative study between the different existing data quality models. {\textcopyright} 2020 ACM.},
	keywords = {Big data, Comparative studies, Complex Processes, data quality, Data quality assessment, Data quality models, Data reduction, Data-sources, Intelligent systems, Large amounts, Large data},
	doi = {10.1145/3419604.3419803},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096426287\&doi=10.1145\%2f3419604.3419803\&partnerID=40\&md5=7542ef0e44f4d94b808c64ce466a5eca},
	author = {Reda, O. and Sassi, I. and Zellou, A. and Anter, S.}
}
@article {11406228320150701,
	title = {The impact of data accuracy on user-perceived business service{\textquoteright}s quality.},
	journal = {CISTI (Iberian Conference on Information Systems \& Technologies / Confer{\^e}ncia Ib{\'e}rica de Sistemas e Tecnologias de Informa{\c c}{\~a}o) Proceedings},
	volume = {2},
	year = {2015},
	pages = {145 - 148},
	abstract = {As business processes have become increasingly automated, data quality becomes the limiting and penalizing factor in the business service{\textquoteright}s overall quality, and thus impacts customer satisfaction, whether it is an end-user, an institutional partner or a regulatory authority. The available research that is related to business services{\textquoteright} quality paid very little attention to the impact of poor data quality on good services delivery and customer satisfaction, and to the calculation of the optimal level of data quality. The aim of this paper is to present a customer-oriented approach that will help to understand and analyze how an organization business service{\textquoteright}s overall quality is linked to the quality of upstream business processes and of data objects in use. This paper also introduces a calculation framework that allows the identification of an optimal level of data quality {\textendash} data accuracy dimension in the case of this paper - taking into account the business processes{\textquoteright} execution accura},
	keywords = {accuracy, Business process management {\textendash} Research, Business process outsourcing, Business service and process quality, Consumers {\textendash} Attitudes, Customer satisfaction {\textendash} Research, data quality, Data quality {\textendash} Research, enterprise architecture, user satisfaction},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=114062283\&site=ehost-live},
	author = {Belhiah, Meryam and Bounabat, Bouchalb and Achchab, Said}
}
