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.

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.

Creating “(Social) Network Art” with NodeXL

2017 ◽  
pp. 342-393
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Network Analysis ◽  
Web 2.0 ◽  
Data Visualization ◽  
Network Graph ◽  
Social Media Platforms ◽  
Machine Art ◽  
Social Sharing

If human-created objects of art are historically contingent, then the emergence of (social) network art may be seen as a product of several trends: the broad self-expression and social sharing on Web 2.0; the application of network analysis and data visualization to understand big data, and an appreciation for online machine art. Social network art is a form of cyborg art: it melds data from both humans and machines; the sensibilities of humans and machines; and the pleasures and interests of people. This chapter will highlight some of the types of (social) network art that may be created with Network Overview, Discovery and Exploration for Excel (NodeXL Basic) and provide an overview of the process. The network graph artwork presented here were all built from datasets extracted from popular social media platforms (Twitter, Flickr, YouTube, Wikipedia, and others). This chapter proposes some early aesthetics for this type of electronic artwork.

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.  

Context collapse and anonymity among queer Reddit users

2019 ◽  
pp. 146144481989035 ◽  
Author(s):  
Anthony Henry Triggs ◽  
Kristian Møller ◽  
Christina Neumayer
Keyword(s):  
Social Media ◽  
Big Data ◽  
Digital Media ◽  
Queer People ◽  
Social Media Platforms ◽  
Queer Communities ◽  
Four Levels

This article maps out how people in queer communities on Reddit navigate context collapse. Drawing upon data from interviews with queer Reddit users and insights from other studies of context collapse in digital media, we argue that context collapse also occurs in anonymity-based social media. The interviews reveal queer Reddit users’ practices of context differentiation, occurring at four levels: somatic, system, inter-platform and intra-platform. We use these levels to map out how lesbian, gay, bisexual, transgender and queer or questioning (LGBTQ) people express their identities and find community on Reddit while seeking to minimize the risks imposed by multiple impending context collapses. Because living an authentic queer life can make subjects vulnerable, we find that despite Reddit’s anonymity, sophisticated practices of context differentiation are developed and maintained. We argue that context collapse in an era of big data and social media platforms operates beyond the control of any one user, which causes problems, particularly for queer people.

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 Tourism trend and network analysis utilizing big data on social media

2018 ◽  
Vol 7 (2.12) ◽  
pp. 312
Author(s):  
Jae Woo Choi ◽  
Hye YoungKim
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mutual Interaction ◽  
Tourism Promotion ◽  
Promotional Efforts ◽  
Social Media Platforms ◽  
Twitter Account ◽  
User Groups ◽  
Media Platform ◽  
Promotional Strategies

Background/Objectives: With evolving trends, tourism is also experiencing more diverse policies and methods of promotion. In particular, with the development and increasing popularity of social media platforms, a new trend is setting in. In line with such changes, the current study sets out to utilize big data on social media platforms to analyze trends in tourism, ways in which tourism elements mutually interact, and analyze patterns, in order to propose tourism promotion strategies and provide related basic data.Methods/Statistical analysis: Analysis on social media platforms were conducted to visually express relationship among nodes and analyze the structure and status of link in quantitative terms. NodeXL is an add-in program to Microsoft Excel; it allows the user to directly collect data from social media platforms to execute matrics, statistics, and visualization. The data was collected from Korea Tourism Organization (KTO)’s Twitter and Facebook accounts. Hashtags (#) on 3,200 posts on the Twitter account were analyzed to compute the tourism trend, and the inter-node interactions and links on the Facebook fan pages were analyzed in terms of network density and centrality to calculate the form and characteristics of social media networks.Findings: By analyzing social media pages that represent promotional efforts for Korean tourism, we were able to find the following results: On the KTO Twitter account, the higher hashtag terms were “eating tour,” and “exciting travel,” which follow the recent tourism trends. However, because of platform restrictions, the Twitter account, rather than engaging in mutual interactions with its users, only tended to deliver information, and was unable to reflect more diverse tourism trends. On Facebook, 348 nodes were actively linked 14.99 times on average, indicating a healthy level of activity. Average degrees of connection was 2.214, which is smaller than average connection distance of small societies, indicating efficient mutual interaction. There were three core user groups, with eleven individuals serving as media nodes, and six users with Eigenvector centrality.Improvements/Applications: Tourism promotion must be executed in line with diverse and latest trends in the field. Because Facebook has a higher level of mutual interaction than Twitter, the account holder can maximize the promotional effects by utilizing individuals that serve as the centrality node. That is to say that promotional strategies that take into account the characteristics of individual social media platform are required. 

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 The role of emotional marketing and UTAUT on donation intention through social media

2021 ◽  
Vol 10 (1) ◽  
pp. 38-46
Author(s):  
Desi Tri Kurniawati ◽  
Nadiyah Hirfiyana Rosita ◽  
Rila Anggraeni
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Real Condition ◽  
Structural Equation Analysis ◽  
Online Platforms ◽  
Equation Analysis ◽  
Social Media Platforms ◽  
Donation Intention ◽  
Willingness To Use

Donations through social media or any online platforms are becoming a new trend these days, thanks to the use of emotional marketing through narrations and visual depictions showing the real condition of people who need supports. Organizations are led to raise people’s emotions to increase their intention to make donations. This study aims to examine the effect of emotional marketing on donation intention through social media platforms and people’s willingness to use technology (UTAUT). This is explanatory research was conducted through a survey on 365 respondents of Malang city who had seen a crowdfunding commercial of Kitabisa.com. The structural equation analysis has led to findings that emotional marketing significantly influences people’s donation intention, implying that the commercial is able to affect people’s emotion into empathy and willingness to make donations through the charity campaign. Furthermore, this study also finds that UTAUT has a significant effect on the intention. The findings are useful for Kitabisa.com in their effort to increase people’s donation intention through the use of emotional marketing.

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