Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories

Natural hazard-related disasters are disruptive events with significant impact on people, communities, buildings, infrastructure, animals, agriculture, and environmental assets. The exponentially increasing anthropogenic activities on the planet have aggregated the climate change and consequently increased the frequency and severity of these natural hazard-related disasters, and consequential damages in cities. The digital technological advancements, such as monitoring systems based on fusion of sensors and machine learning, in early detection, warning and disaster response systems are being implemented as part of the disaster management practice in many countries and presented useful results. Along with these promising technologies, crowdsourced social media disaster big data analytics has also started to be utilized. This study aims to form an understanding of how social media analytics can be utilized to assist government authorities in estimating the damages linked to natural hazard-related disaster impacts on urban centers in the age of climate change. To this end, this study analyzes crowdsourced disaster big data from Twitter users in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method and conducts sentiment and content analyses of location-based Twitter messages (n = 131,673) from Australia. The study informs authorities on an innovative way to analyze the geographic distribution, occurrence frequency of various disasters and their damages based on the geo-tweets analysis.

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Review of social media analytics process and Big Data pipeline

Social Network Analysis and Mining ◽

10.1007/s13278-018-0507-0 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 4

Author(s):

Hiba Sebei ◽

Mohamed Ali Hadj Taieb ◽

Mohamed Ben Aouicha

Keyword(s):

Social Media ◽

Big Data ◽

Social Media Analytics ◽

Data Pipeline

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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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Social Media Analytics

10.4018/978-1-7998-8528-3.ch021 ◽

2022 ◽

pp. 385-410

Author(s):

Časlav Kalinić ◽

Miroslav D. Vujičić

Keyword(s):

Social Media ◽

Big Data ◽

Cultural Tourism ◽

Social Media Analytics ◽

Online Platforms ◽

New Information ◽

People Participation ◽

Management And Development ◽

Social Media Platforms ◽

Tourism Destinations

The rise of social media allowed greater people participation online. Platforms such as Facebook, Twitter, Instagram, or TikTok enable visitors to share their thoughts, opinions, photos, locations. All those interactions create a vast amount of data. Social media analytics, as a way of application of big data, can provide excellent insights and create new information for stakeholders involved in the management and development of cultural tourism destinations. This chapter advocates for the employment of the big data concept through social media analytics that can contribute to the management of visitors in cultural tourism destinations. In this chapter, the authors highlight the principles of big data and review the most influential social media platforms – Facebook, Twitter, Instagram, and TikTok. On that basis, they disclose opportunities for the management and marketing of cultural tourism destinations.

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Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics

IEEE Access ◽

10.1109/access.2020.2991394 ◽

2020 ◽

Vol 8 ◽

pp. 82215-82226 ◽

Cited By ~ 6

Author(s):

Arun Kumar Sangaiah ◽

Alireza Goli ◽

Erfan Babaee Tirkolaee ◽

Mehdi Ranjbar-Bourani ◽

Hari Mohan Pandey ◽

...

Keyword(s):

Social Media ◽

Big Data ◽

Computing System ◽

Data Driven ◽

Social Media Analytics ◽

Cognitive Computing

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Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc6040187 ◽

2020 ◽

Vol 6 (4) ◽

pp. 187 ◽

Cited By ~ 2

Author(s):

Tan Yigitcanlar ◽

Nayomi Kankanamge ◽

Massimo Regona ◽

Andres Ruiz Maldonado ◽

Bridget Rowan ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Urban Planning ◽

Methodological Approach ◽

Digital Transformation ◽

Social Media Analytics ◽

Urban Context ◽

Banking And Finance ◽

Urban Planning And Development ◽

Artificial Creativity

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

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Big Data Social Media Analytics for Purchasing Behaviour

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.36.23917 ◽

2018 ◽

Vol 7 (4.36) ◽

pp. 463

Author(s):

Shahid Shayaa ◽

Ainin Sulaiman ◽

Arsalan Zahid Piprani ◽

Mohammed Ali Al-Garadi ◽

Muhammad Ashraf

Keyword(s):

Social Media ◽

Big Data ◽

Social Media Analytics ◽

Purchasing Behavior ◽

The Social ◽

Purchasing Behaviour

The social media is rich in data and of late its data have been used for various types of analytics. This paper examines the purchasing behavior and sentiments of social media users from Jan - 2015 to Dec – 2016. The purchasing behaviour of the users is categorized into five: buy car, buy house, buy computer, buy hand phone and going for holiday. The paper will also demonstrate the trend of each individual category. The results of the analysis would provide businesses information on the social media users’ purchasing behavior, their sentiment thus allowing them to take more appropriate strategies to enhance their competitiveness.

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Understanding an Urban Park through Big Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16203816 ◽

2019 ◽

Vol 16 (20) ◽

pp. 3816 ◽

Cited By ~ 5

Author(s):

Jisoo Sim ◽

Patrick Miller

Keyword(s):

Social Media ◽

Big Data ◽

Data Analytics ◽

Planning Process ◽

Big Data Analytics ◽

Urban Park ◽

Social Media Analytics ◽

Forest Park ◽

Planning And Design ◽

Data Source

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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Media Sosial Universitas Muhammadiyah Yogyakarta @UMYogya dalam Perspektif Social Big Data

Jurnal Riset Komunikasi ◽

10.38194/jurkom.v4i1.194 ◽

2021 ◽

Vol 4 (1) ◽

pp. 81-97

Author(s):

Ayu Amalia

Keyword(s):

Social Media ◽

Big Data ◽

Social Media Analytics ◽

Social Data ◽

Social Big Data

Social big data merupakan potensi pengelolaan big data dengan pendekatan baru yang spesifik merujuk pada data-data yang dihasilkan dari media sosial. Universitas Muhammadiyah Yogyakarta sebagai institusi pendidikan tinggi yang bereputasi dengan media sosial @UMYogya, merupakan kontributor potensial dalam konteks big data. Penelitian ini merupakan penelitian deskriptif yang bertujuan mengungkap potensi media sosial @UMYogya secara kuantitaif dengan menggunakan alat bantu berupa fitur social media analytics. Media sosial sebagai mid-tier influencer memiliki intensitas engagement dengan viewers media sosialnya pada taraf yang cukup signifikan, dengan menerapkan skema pengelolaan media sosial yang merujuk pada diagram big social data, maka media sosial @UMYogya sebagai representasi dari Universitas Muhammadiyah Yogyakarta dapat meningkatkan impact-nya dengan lebih melibatkan stakeholders dan mengembangkan media sosialnya dengan merujuk pada potensi social big data itu sendiri.

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