Understanding an Urban Park through Big Data

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

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

Asian Journal of University Education ◽

10.24191/ajue.v16i4.11945 ◽

2021 ◽

Vol 16 (4) ◽

pp. 82

Author(s):

Muhamad Hariz Muhamad Adnan ◽

Shamsul Arrieya Ariffin ◽

Hafizul Fahri Hanafi ◽

Mohd Shahid Husain ◽

Ismail Yusuf Panessai

Keyword(s):

Higher Education ◽

Social Media ◽

Big Data ◽

Data Analytics ◽

Role Model ◽

Higher Education Institutions ◽

Big Data Analytics ◽

Vocational Education And Training ◽

Social Media Analytics ◽

Prospective Students

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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EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

Journal of Public Policy & Marketing ◽

10.1177/0743915621999674 ◽

2021 ◽

pp. 074391562199967

Author(s):

Raffaello Rossi ◽

Agnes Nairn ◽

Josh Smith ◽

Christopher Inskip

Keyword(s):

Social Media ◽

Big Data ◽

Content Analysis ◽

Volume Content ◽

Data Analytics ◽

Big Data Analytics ◽

Regulatory Compliance ◽

The Internet ◽

Policy Recommendations ◽

Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

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Big Data Analytics and Social Media in Disaster Management

Integrating Disaster Science and Management ◽

10.1016/b978-0-12-812056-9.00016-6 ◽

2018 ◽

pp. 287-294 ◽

Cited By ~ 3

Author(s):

Joice K. Joseph ◽

Karunakaran Akhil Dev ◽

A.P. Pradeepkumar ◽

Mahesh Mohan

Keyword(s):

Social Media ◽

Big Data ◽

Disaster Management ◽

Data Analytics ◽

Big Data Analytics

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Challenges and Uses of Big Data Analytics for Social Media

Lecture Notes in Electrical Engineering - ICDSMLA 2019 ◽

10.1007/978-981-15-1420-3_118 ◽

2020 ◽

pp. 1074-1081

Author(s):

Mudassir Khan ◽

Mohd Dilshad Ansari ◽

Syed Yasmeen Shahdad

Keyword(s):

Social Media ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Big Data and Analytics

Big Data Analytics for Sustainable Computing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-9750-6.ch003 ◽

2020 ◽

pp. 47-65

Author(s):

Sheik Abdullah A. ◽

Priyadharshini P.

Keyword(s):

Social Media ◽

Big Data ◽

Predictive Analytics ◽

Data Warehousing ◽

Big Data Analytics ◽

Structured Data ◽

Social Media Analytics ◽

Data Set ◽

Data Volume ◽

Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

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Predictive Analysis for Digital Marketing Using Big Data

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch016 ◽

2017 ◽

pp. 259-283

Author(s):

Balamurugan Balusamy ◽

Priya Jha ◽

Tamizh Arasi ◽

Malathi Velu

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Big Data ◽

Statistical Analysis ◽

Data Analytics ◽

Big Data Analytics ◽

Predictive Analysis ◽

Cognitive Computing ◽

Internet Resources ◽

To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

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