Social media analytics based on big data

Shaikh, Farzana; Rangrez, Firdaus; Khan, Afsha; Shaikh, Uzma

doi:10.1109/i2c2.2017.8321806

Cited by 7 publications

(9 citation statements)

References 3 publications

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“…The application of big data analytics for aviation is necessary as latest aircrafts like the Boeing 787 obtains 1000 or more flight parameters, whereas older aircrafts like Legacy captured only 125+ parameters [136]. Similarly, social media platforms like Facebook, Instagram, and Twitter generate data, its analysis is necessary to understand and gather public opinion or feedback about any product or service [18,137], which can be analyzed using machine learning applications of big data. Machine learning algorithms are used to analyze the behavior of the user via real-time analysis of the content browsed by them, and relevant online advertisements are recommended accordingly.…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

“…This calls for a system to be able to effectively manage these data and filter the data related to the needs and requests of the people during the post-disaster period. To be able to provide timely help, the big data generated from the social networks should be mined and analyzed to determine factors like which areas need the most relief services and should be prioritized by the relief workers, and what services are required by the people there [137]. In this section, we propose a framework that extracts the data from various social media networks like Facebook, Twitter, news APIs, and other sources.…”

Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

“…The integration of big data into disaster risk management planning can open many new avenues. At the time of disasters like floods, bush fires, storms, etc., there is a bulk of data generated as new reports, statistics, and social media posts, which all provide a tally of injuries, deaths, and other losses incurred [77,83,137]. An overview of the suggested system is provided by Figure 14.…”

Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

See 2 more Smart Citations

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

106

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

See 1 more Smart Citation

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

106

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“…Three main social media are selected as the main platform to build awareness and share content with the audiences. The reason why Facebook, Twitter and Instagram are chosen is that: 1) all of these social media have many users; 2) the majority of the prospective students (teenagers and young adults) use these social media; and 3) these social media platforms provide insight functions that can be used for data analytics (Busalim et al, 2019;Curlin et al, 2019;Ghani et al, 2019;Irfan et al, 2017;Katal et al, 2013;Nalwoga Lutu, 2019;Schaffer & Debb, 2020;Shaikh et al, 2018;Stieglitz et al, 2018;Thomas et al, 2020;Yanai et al, 2019). Different types of content will be used including news, information, activities and updates in the format of texts, images, audios and videos to increase the awareness of prospective students of the benefits of STEM and TVET subjects.…”

Section: Social Mediamentioning

confidence: 99%

“…For instance, the big data from social media were applied for analytics, trend identifications, opinion mining and sentiment analysis (Katal, Wazid, & Goudar, 2013). Yet, Twitter is one of the prominent sources of social media big data with millions of generated daily tweets (Shaikh, Rangrez, Khan, & Shaikh, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Adnan

Ariffin

Hanafi

et al. 2021

AJUE

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Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective students’ interest in STEM and also Technical and Vocational Education and Training (TVET) subjects. However, very little research has been done for the higher education institutions in Malaysia in leveraging social media and social media analytics effectively to increase the students’ interests and awareness of STEM and TVET disciplines. Therefore, this paper aims to propose a framework to increase prospective students’ interest in STEM and TVET using social media and big data analytics. The objectives of this study are to explore various social media applications in education and study these applications towards increasing students’ interests and propose a suitable framework for Malaysian higher education institutions. The framework is proposed by following the theory synthesis methodology. Four main components of the framework have been proposed, namely social media, role model or mentoring, massive open online courses and big data analytics. Each component is significant and requires a considerable amount of time to develop. The suggested framework is anticipated to benefit higher education institutions with a significant gain of the number of students, revenues and positive reputations. Keywords: Social media, Social media analytics, STEM, E-learning, Education

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Big Data Analytics in Social Media: A Triple T (Types, Techniques, and Taxonomy) Study

Rahman

Reza

2021

Advances in Intelligent Systems and Computing

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Social media analytics based on big data

Cited by 7 publications

References 3 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Big Data Analytics in Social Media: A Triple T (Types, Techniques, and Taxonomy) Study

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