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.  

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 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.

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

scholarly journals The Impact of Media Advertisement on Consumers Purchasing Behaviour

Patan Pragya ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 268-278
Author(s):  
Gunja Kumari Sah ◽  
Sangita Karki
Keyword(s):  
Social Media ◽  
Consumer Behavior ◽  
Purchase Behavior ◽  
Purchasing Behavior ◽  
Media Platform ◽  
Purchasing Behaviour ◽  
Media Habits ◽  
The Impact ◽  
The Relationship ◽  
Structured Questionnaire

Marketers spend a massive amount on various media platforms to influence consumer behavior. Advertisement on every media platform has a different component that involves the Consumer for different purposes. Technological innovation has led to changes in Consumer's media habits. Hence, a deeper understanding of advertisements on various media platforms, and their implications on consumer behavior needs to be established. This study aims to examine the relationship between advertisement dimensions such as printing, broadband, outdoor and social media, and consumer purchasing behavior. Data were collected with the help of a structured questionnaire by email and direct interviews with the consumers located in Kathmandu valley. The findings revealed that the advertisement media dimensions had a strong correlation with consumer purchasing behavior. It also indicated that printing, outdoor and social media were statistically significant, and broadband media were found to be statistically insignificant with consumers 'purchase behavior

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.

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