scholarly journals Social business intelligence: Review and research directions

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Helena Gioti ◽  
Stavros T. Ponis ◽  
Nikolaos Panayiotou
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Scientific Field ◽  
Added Value ◽  
Data Systems ◽  
Social Business ◽  
Research Directions ◽  
Wide Range ◽  
Initial Shock ◽  
Media Data

Social business intelligence (SBI) is a rather novel discipline, emerged in theacademic and business literature as a result of the convergence of two distinct researchdomains: business intelligence (BI) and social media. Traditional BI scientists and practitioners,after an inevitable initial shock, are currently discovering and acknowledge the potential of usergenerated content (UGD) published in social media as an invaluable and inexhaustible sourceof information capable of supporting a wide range of business activities. The confluence of thesetwo emerging domains is already producing new added value organizational processes andenhanced business capabilities utilized by companies all over the world to effectively harnesssocial media data and analyze them in order to produce added value information such ascustomer profiles and demographics, search habits, and social behaviors. Currently the SBIdomain is largely uncharted, characterized by controversial definitions of terms and concepts,fragmented and isolated research efforts, obstacles created by proprietary data, systems andtechnologies that are not mature yet. This paper aspires to be one of the few -to our knowledge contemporaryefforts to explore the SBI scientific field, clarify definitions and concepts,structure the documented research efforts in the area and finally formulate an agenda of futureresearch based on the identification of current research shortcomings and limitations.

scholarly journals Business intelligence for social media interaction in the travel industry in Indonesia

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Michael Yulianto ◽  
Abba Suganda Girsang ◽  
Reinert Yosua Rumagit
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Marketing Activity ◽  
Social Media Data ◽  
Current Technology ◽  
Media Interaction ◽  
Travel Industry ◽  
Media Data ◽  
Analyze Data

Electronic ticket (eticket) provider services are growing fast in Indonesia, makingthe competition between companies increasingly intense. Moreover, most of them have the sameservice or feature for serving their customers. To get back the feedback of their customers, manycompanies use social media (Facebook and Twitter) for marketing activity or communicatingdirectly with their customers. The development of current technology allows the company totake data from social media. Thus, many companies take social media data for analyses. Thisstudy proposed developing a data warehouse to analyze data in social media such as likes,comments, and sentiment. Since the sentiment is not provided directly from social media data,this study uses lexicon based classification to categorize the sentiment of users’ comments. Thisdata warehouse provides business intelligence to see the performance of the company based ontheir social media data. The data warehouse is built using three travel companies in Indonesia.As a result, this data warehouse provides the comparison of the performance based on the socialmedia data.

scholarly journals Predictive modeling to study lifestyle politics with Facebook likes

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Stiene Praet ◽  
Peter Van Aelst ◽  
Patrick van Erkel ◽  
Stephan Van der Veeken ◽  
David Martens
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Party System ◽  
Political Polarization ◽  
Daily Lives ◽  
Political Preference ◽  
Social Media Data ◽  
Cultural Preferences ◽  
Wide Range ◽  
Media Data

Abstract“Lifestyle politics” suggests that political and ideological opinions are strongly connected to our consumption choices, music and food taste, cultural preferences, and other aspects of our daily lives. With the growing political polarization this idea has become all the more relevant to a wide range of social scientists. Empirical research in this domain, however, is confronted with an impractical challenge; this type of detailed information on people’s lifestyle is very difficult to operationalize, and extremely time consuming and costly to query in a survey. A potential valuable alternative data source to capture these values and lifestyle choices is social media data. In this study, we explore the value of Facebook “like” data to complement traditional survey data to study lifestyle politics. We collect a unique dataset of Facebook likes and survey data of more than 6500 participants in Belgium, a fragmented multi-party system. Based on both types of data, we infer the political and ideological preference of our respondents. The results indicate that non-political Facebook likes are indicative of political preference and are useful to describe voters in terms of common interests, cultural preferences, and lifestyle features. This shows that social media data can be a valuable complement to traditional survey data to study lifestyle politics.

Social Media and Corporate Data Warehouse Environments

2012 ◽  
Vol 3 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Debora S. Bartoo
Keyword(s):  
Social Media ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Warehouses ◽  
Social Media Data ◽  
Hands On ◽  
Media Data

This paper argues that organizations need to prepare for the integration of social media data into their data warehouses in order to fully understand their customers. Social media has quickly gained acceptance in its adoption and use and firms are eager to get their hands on it to better understand customer sentiment. However, social media data is different and more complex than traditional data and most data warehouses are not structured in a way for BI applications to easily make sense it. As a result, it is becoming critical for business intelligence teams to begin to understand the challenges this data presents and to better plan for the integration of this information into corporate data warehouses.

scholarly journals Optimizing Search and Data Analytics of Twitter Data using Elastic Search Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 427-433
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Data Analytics ◽  
Distributed Search ◽  
Time Data ◽  
Social Media Data ◽  
Twitter Data ◽  
Crucial Part ◽  
Media Data ◽  
Better Than

Quick data acquisition and analysis became an important tool in the contemporary era. Real time data is made available in World Wide Web (WWW) and social media. Especially social media data is rich in opinions of people of all walks of life. Searching and analysing such data provides required business intelligence (BI) for applications of various domains in the real world. The application may be in the area of politics or banking or insurance or healthcare industry. With the emergence of cloud computing, volumes of data are added to cloud storage infrastructure and it is growing exponentially. In this context, Elasticsearch is the distributed search and analytics engine that is very crucial part of Elastic Stack. For data collection, aggregation and enriching it Beats and Logstash are used and such data is stored in Elasticsearch. For interactive exploration and visualization Kibana is used. Elasticsearch helps in indexing of data, searching efficiently and performing data analytics. In this paper, the utility of Elasticsearch is evaluated for optimising search and data analytics of Twitter data. Empirical study is made with the Elasticsearch tool configured for Windows and also using Amazon Elasticsearch and the results are compared with state of art. The experimental results revealed that the Elasticsearch performs better than the existing ones.

scholarly journals Business Intelligence Model to analyze Social Media through Big Data analytics

2021 ◽  
Vol 119 ◽  
pp. 07006
Author(s):  
Kawtar Mouyassir ◽  
Mohamed Hanine ◽  
Hassan Ouahmane
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Qualitative Data ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Social Business ◽  
Social Media Networks ◽  
Big Data Technologies ◽  
Voice Of Customer

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

scholarly journals Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering

2020 ◽  
Vol 9 (4) ◽  
pp. 245 ◽  
Author(s):  
Ayse Giz Gulnerman ◽  
Himmet Karaman
Keyword(s):  
Social Media ◽  
Spatial Information ◽  
Similarity Index ◽  
Spatial Accuracy ◽  
Social Media Data ◽  
Investigation Methods ◽  
Wide Range ◽  
Similarity Indices ◽  
Media Data ◽  
Filtering Techniques

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.

Managing extracted knowledge from big social media data for business decision making

2017 ◽  
Vol 21 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Wu He ◽  
Feng-Kwei Wang ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Business Decisions ◽  
Business Decision ◽  
Social Media Data ◽  
Content Type ◽  
Media Data ◽  
Big Data Technology

Purpose This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors. Design/methodology/approach A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data. Findings This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence. Originality/value Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.

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