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

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.

Review of social media analytics process and Big Data pipeline

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Hiba Sebei ◽  
Mohamed Ali Hadj Taieb ◽  
Mohamed Ben Aouicha
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Media Analytics ◽  
Data Pipeline

Big Data and Analytics

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.

Social Media Analytics

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.

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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82215-82226 ◽  
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

scholarly journals Big Data Social Media Analytics for Purchasing Behaviour

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.  

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
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.

scholarly journals ANALYSIS ON COGNITIVE SOCIAL DATA FUSION

2020 ◽  
pp. 27-30
Author(s):  
Anandakumar H ◽  
Tamilselvan T ◽  
Nandni S ◽  
Subashree R ◽  
Vinodhini E
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Data Processing ◽  
Pattern Mining ◽  
Data Representation ◽  
Map Reduce ◽  
User Interest ◽  
Social Data ◽  
Algorithm Mapping

Big data stands for effective handling of large amount of data, research, mining, intelligence. In social media large amount of data uploaded every.Social media handle large amount of data like photo, video, songs and so many using big data. When it comes for big data, a large amount of data should be effectively handled. Big data face various challenges like clustering of data, visualizing, data representation, data processing, pattern mining, tracking of data and analysing behaviour of users. In this paper the Emoji in messages are decoded and Unicode will be set. Based on the Emoji the user interest can be understood in a better way. Then another part involves the replacement of repeated data by using the map Reduce algorithm. Mapping of data with key values used to reduce the size of storage.

Detecting customers knowledge from social media big data: toward an integrated methodological framework based on netnography and business analytics

2020 ◽  
Vol 24 (4) ◽  
pp. 799-821 ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Giuseppina Passiante ◽  
Demetris Vrontis ◽  
Cosimo Fanuli
Keyword(s):  
Social Media ◽  
Big Data ◽  
Customer Relationship Management ◽  
Value Creation ◽  
Relationship Management ◽  
Customer Relationship ◽  
Business Analytics ◽  
Content Type ◽  
Social Big Data ◽  
Practical Implications

Purpose This paper aims to demonstrate how the integration of netnography and business analytics can support companies in the process of value creation from social big data by leveraging on customer relationship management and customer knowledge management (CKM). Design/methodology/approach This paper adopts the methodology of a single case study by using desk analysis, netnography and business analytics. The context of analysis has been identified into the case of Aurora Company, a well-known producer of fountain pens. Findings The case demonstrates how the integration of big data analytics and netnography is relevant for the development of a customer relationship management strategy. The results obtained have been categorized according to the three main categories of customer knowledge, such as knowledge for, from and about customer. Research limitations/implications This paper presents implications for the advancement of the theory on CKM by demonstrating, as the acquisition, storage and management of data generated by customers on social media require the adoption of a cross-disciplinary approach resulting from the integration of qualitative and quantitative approaches. The framework is structured as methodological tool to detect knowledge in virtual community. Practical implications Practical implications arise for managers and entrepreneurs in terms of value creation from knowledge assets generated on social big data through the management of the customers’ relationship and data-driven innovation patterns. Originality/value This paper offers an original contribution of integration of well-established research streams. The focus on the knowledge under the perspectives of information assets for, from and about customers in the debate on value creation and management of big data is an element of value offered by this study in addition to the comprehension of strategies of social customer relationship management as actual initiative embraced by a company in the leveraging of innovation and tradition.

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