Strengths and Limitations of Social Media Analytics Tools

The aim of this chapter is to research and present strengths and limitations of social media analytics tools used in the financial sector. Emphasis is on the business point of view that sees the social media analytics as a collection of tools that transform semi-structured and unstructured social data into noteworthy business insight. There are two main aspects of social media analytics: the technology aspect which covers identifying, extracting, and analyzing social media data using sophisticated tools and techniques; and the business aspect which interprets the data findings and aligns them with business goals. Namely, it is simply not enough to have a social media analytics tool; the tool should be strategically aligned to support existing business goals. The chapter offers a framework for easier adoption and implementation of these tools in the financial sector.

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Social Media to Social Media Analytics

International Journal of Technoethics ◽

10.4018/ijt.2019070104 ◽

2019 ◽

Vol 10 (2) ◽

pp. 57-70 ◽

Cited By ~ 4

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.

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Identifying customer knowledge on social media through data analytics

Journal of Enterprise Information Management ◽

10.1108/jeim-02-2018-0031 ◽

2019 ◽

Vol 32 (1) ◽

pp. 152-169 ◽

Cited By ~ 17

Author(s):

Wu He ◽

Weidong Zhang ◽

Xin Tian ◽

Ran Tao ◽

Vasudeva Akula

Keyword(s):

Social Media ◽

Large Scale ◽

Social Media Analytics ◽

Data Set ◽

Social Media Data ◽

Customer Knowledge ◽

Content Type ◽

Customer Knowledge Management ◽

The Social ◽

Media Data

Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge. Findings The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers. Originality/value This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

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Using social media data to map the areas most affected by ISIS in Syria

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2020-1-26-464-470 ◽

2020 ◽

Vol 26 (1) ◽

pp. 464-470

Author(s):

Mohamad Hasan

Keyword(s):

Social Media ◽

Language Processing ◽

Geographical Information ◽

Data Mapping ◽

Islamic State ◽

Social Media Data ◽

The Social ◽

Mapping Process ◽

Data Source ◽

Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

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Social Media Analytics: Data Utilization of Social Media for Research

Journal of Information Systems and Informatics ◽

10.33557/journalisi.v1i2.23 ◽

2019 ◽

Vol 1 (2) ◽

pp. 193-205

Author(s):

Ria Andryani ◽

Edi Surya Negara ◽

Dendi Triadi

Keyword(s):

Social Media ◽

Public Perception ◽

Data Retrieval ◽

Media Analysis ◽

Internet Technology ◽

Mining Machine ◽

Social Media Analytics ◽

Social Media Data ◽

Visualization Techniques ◽

Media Data

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.

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Destination image through social media analytics and survey method

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-08-2020-0861 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Michael S. Lin ◽

Yun Liang ◽

Joanne X. Xue ◽

Bing Pan ◽

Ashley Schroeder

Keyword(s):

Social Media ◽

Survey Data ◽

Destination Image ◽

Social Media Analytics ◽

Tourism Destination ◽

Social Media Data ◽

Content Type ◽

Holistic Understanding ◽

Intercept Survey ◽

Media Data

Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

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The Social Media Macroscope: A science gateway for research using social media data

Future Generation Computer Systems ◽

10.1016/j.future.2019.10.029 ◽

2020 ◽

Vol 111 ◽

pp. 819-828 ◽

Cited By ~ 1

Author(s):

Joseph T. Yun ◽

Nickolas Vance ◽

Chen Wang ◽

Luigi Marini ◽

Joseph Troy ◽

...

Keyword(s):

Social Media ◽

Social Media Data ◽

Science Gateway ◽

The Social ◽

Media Data

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Review of Data Visualization for Social Media Postings

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.38.27613 ◽

2018 ◽

Vol 7 (4.38) ◽

pp. 939

Author(s):

Nur Atiqah Sia Abdullah ◽

Hamizah Binti Anuar

Keyword(s):

Social Media ◽

Data Visualization ◽

Line Graph ◽

Data Types ◽

Social Media Data ◽

Data Analyst ◽

The Social ◽

Social Media Platforms ◽

Types Of Information ◽

Media Data

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.

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Corrigendum

Journal of Information Science ◽

10.1177/0165551518781507 ◽

2018 ◽

Vol 45 (1) ◽

pp. 136-136

Keyword(s):

Public Health ◽

Social Media ◽

Information Science ◽

Patient Data ◽

Online Version ◽

Social Media Data ◽

Medical Network ◽

The Social ◽

Media Data ◽

Prior Permission

Ji X, Chun SA, Cappellari P, et al. Linking and using social media data for enhancing public health analytics. Journal of Information Science 2016; 43: 221–245. DOI: 10.1177/0165551515625029 The authors regret that non-anonymised patient data was used from a social medical network without prior permission. With permission from the social medical network, the authors have anonymised the data and corrected the article. The online version of the article has been corrected.

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