A taxonomy and survey of big data in social media

World's largest community Facebook's ‘Like’ button pressed 2.7 billion times every day across the web revealing what people care about, such an impact of social media that internet user average almost spends 2.5 hours daily on liking, chatting, poking, tweeting on social media, which has become vast source of unstructured data. While dealing with big data it's difficult for traditional databases and architecture to modify, grill and then structure this data, it can lead to many consumer insights which can help to create win-win situations. It has become necessary to find out value from large data sets to show relationships, dependencies as well as to perform predictions of outcomes and behaviors. Big Data has been characterized by 5 Vs — Volume, Velocity, Variety, Veracity and Value. This paper deals with all these 5Vs, features, challenges, future of Big Data in social media arena using data mining algorithms, tools and Hadoop framework for overcoming challenges of Big Data.

Progress and challenges in the analysis of big data in social media of adolescents Abstract. Social media are ubiquitous today, and adolescents use them to express their thoughts, feelings, and behaviours. New interdisciplinary methods allow the automatic analysis of the massive amounts of data (big data) available on social networking websites using machine-learning tools to detect indicators of mental-health problems and disorders by identifying differences with common activity and communication patterns. This review first introduces the concept and potential fields of applications of big data in social media. It then discusses the first studies that used big data analyses and detected mental-health problems by identifying differences in the structure of social networks, in the use of certain words, and in the communication of opinions and sentiments. Future studies employing several assessment points could use longitudinal mediation analysis to model intraindividual changes in order to understand when and through which mechanisms social media use has an impact on mental health. Furthermore, future studies should include additional mental disorders, various sources of information, a broader age range, and additional social-networking websites to develop more precise models for the early detection of mental disorders. This would enable the development of personalised intervention programs to promote mental health and resilience in adolescents.

Crowd management has become an integral part of urban planning in abnormality in the crowd and predict its future issues. Big data in social media is a rich source for researchers in crowd data analysis. In this systematic literature review (SLR), modern societies. It can organize the flow of the crowd, perform counting, recognize the related works are analyzed, which includes crowd management from both global and local sides (Hajj events—Saudi Arabia) based on deep learning (DL) methods. This survey concerns crowd management research published from 2010 to 2023. It has specified 45 primary studies that accomplish the objectives of the research questions (RQs), namely, investigation of the taxonomies, approaches, and comprehensive studies of crowd management both globally and locally and focusing on the most commonly used techniques of DL. We found both supervised and unsupervised DL techniques have achieved high accuracy, with different strengths and weaknesses for each approach. A lot of these studies discuss aspects of scene analysis of crowds, that are captured by installed cameras in the place. However, there is a dilemma regarding exploiting data provided on social media to use in the crowd analysis domain. Which we believe that the analysis of big data may raise crowd management to the upper level of enhancement. To this end, motivated by the findings of this SLR. The primary purpose of this review is strived to illustrate obstacles and dilemmas in crowd analysis fields to provide a road map for future researchers. Furthermore, it aims to find research gaps existing to focus on it in the future studies. The results indicate that the lack of Hajj research, especially in sentiment analysis and the study of the pilgrims' behavior.

As a cutting-edge technology of modern network technology, the big data is of crucial application value to the development of social media marketing. In this regard, we firstly conduct researches on social media marketing and big data to define their connotation and implementation significance; secondly, after having a knowledge of the development direction and entry of media marketing in the era of big data, we identify the social media marketing changes caused by data; finally, we analyze the precise social media marketing strategy to provide certain clues to the application of big data in social media marketing. Through this study proved that big data will directly affect social media marketing, not only the change of basic marketing, big data relies on data analysis and integration ability, can constantly dig and query the effective market seeking repair information, predict the consumer's consumption behavior, thus building a new mode of precision marketing, is an important development direction of China's social media marketing in the future, worthy of further in-depth study.

Society 2.0; with the help of recent advancements in the internet and web 2.0 technology, makes the social media-based platform the most popular source for big data research. Big Data Analytics contributes by adjusting, analyzing, and forecasting insightful recommendations from this huge source of noisy & mostly unstructured “Big Social Data”. We present 10 mostly used big data analytics in the working domain of social media-based platforms. Different popular techniques or algorithms related to each big data analytic are also listed in this study. We show that “Text Analytics” is the most popular big data analytics in social media data analysis. Through this research, we try to explain the 10 Bigs of big data and introduce the “Sunflower Model of Big Data”. We also explain the reason why the social media-based platform is so significant and popular source of big data by analyzing the most recent statistics. This study will be a handful for all other researchers who want to work with big data in social media and in advance; make their work easy for selecting the best big data analytics method suitable for their research work.

PurposeThis 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/approachA 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.FindingsThis 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/valuePractical 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.

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders. Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit. Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses. This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication.

Big social data and digital technologies create tremendous opportunities but raise questions and concerns on ethical data usage and sharing. Moreover, big social data plays a vital role in Thailand's 20‐year national strategy to turn Thailand into a developed nation by 2037, especially on security and human capital development strategies. Nonetheless, the progress in big social data must go hand‐in‐hand with ethical standards. To date, there are no universal ethical criteria for big social data sharing and governance. This study investigates the ethical issues of big data in social media. It maps big social data to workable ethical theories. The model of big social data sharing factors was proposed. Using Thailand as a case study, the exploratory study examined the digital behaviors and moral perceptions of the millennials' big social data sharing through 71 in‐depth interviews. The results revealed a strong pattern toward “ethical consequentialism” among the Thai millennials. Examining these findings fosters the formation of big social data ethics from the views of the data generators. This study has attempted to contribute to scholarship in the growing body of work on appropriate ethical guidelines for big social data sharing and help Thailand achieve its national strategy.

Optimal search in search engines is very urgent, especially search performs on social media search engine. Very big data in social media have not been not used as many as expected, making the existed data are only limited to the data themselves. However, using keywords on social media search engines could only produces incomplete and inaccurate data thereby the search results only have limited usage. This paper contains a framework using keywords in social media search engines that aims to gain users' habits by utilizing social media data. The framework offered is in the form of steps to optimize the search engines, so that the optimal social media search engine could be used as an entry point to find the desired data on specific social media user. The step is initialized by analyzing existing posts to get more specific and accurate data from social media users. The social media system then could use those specific keywords to identify the identity of a specific user in the information space that can be accessed by the search engines.

It is estimated that, by 2020, 40 zettabytes of data will be created. The convergence of pervasive sensing with locationaware and social media technologies, along with infrastructure-based sensors, will lead to the production and collection of “big data” in many areas such as transportation, healthcare, and energy. For example, today, there are 6 billion cell phone users in the world. Cell phones equipped with multiple sensors are producing large volumes of data each day. The data obtained may be structured or unstructured, ranging from GPS trajectories to text, video, still images, and others. This opens up new challenges and opportunities to address the key aspects of sensor-based big data, namely, volume, velocity, variety, and veracity. This special issue aims to foster the dissemination of knowledge for advanced issues in big data management and analytics for ubiquitous sensors. This special issue will be an open international forum for researchers to summarize their latest research results. The call for papers included a number of related topics such as distributed/parallel processing of streaming data, privacy protection and security issues in sensor-based big data, and data fusion techniques for distributed big data. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. The paper by I. Ha et al. proposes a parallel approach usingMapReduce for sentiment analysis of big data in social media. The paper by Y. Yu et al. presents a parallel approach using Hadoop for density-based clustering of big data. The paper by K. Omote and T. P. Thao describes a light-weight network coding scheme to provide integrity of the data when stored in cloud servers. The paper by H.-J. Jo and J. W. Yoon presents a countermeasure to prevent bruteforce attacks in high-performance computing platforms for big data analytics. The papers by H. Kang et al. and Y. Ki et al. propose new analysis-based approaches to detect malware in mobile/smart devices. Finally, the paper by S.-W. Jang G.Y. Kim presents a multiple feature-based image switching strategy in visual sensor networks.

Background: As the use of social media creates huge amounts of data, the need for big data analysis has to synthesize the information and determine which actions is generated. Online communication channels such as Facebook, Twitter, Instagram etc provide a wealth of passively collected data that may be mined for public health purposes such as health surveillance, health crisis management, and last but not least health promotion and education. Objective: We explore international bibliography on the potential role and perceptive of use for social media as a big data source for public health purposes. Method: Systematic literature review. Data extraction and synthesis was performed with the use of thematic analysis. Results: Examples of those currently collecting and analyzing big data from generated social content include scientists who are working with the Centers for Disease Control and Prevention to track the spread of flu by analyzing what user searches, and the World Health Organization is working on disaster management relief. But what exactly do we do with this big social media data? We can track real-time trends and understand them quicker through the platforms and processing services. By processing this big social media data, it is possible to determine specific patterns in conversation topics, users behaviors, overall trends and influencers, sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Conclusion: The key to fostering big data and social media converge is process and analyze the right data that may be mined for purposes of public health, so as to provide strategic insights for planning, execution and measurement of effective and efficient public health interventions. In this effort, political, economic and legal obstacles need to be seriously considered.

Social media contributes much to big data. Among the 4V characteristics of big data, this article focuses on investigating the in big social media data. Social media variety mainly concerns with the heterogeneous user behaviors in differenet social media networks. Understanding into social emdia variety plays important roles in insightful social media analysis and comprehensive social media applications. Social meida is typically generated from user and desinged for user services. We propose to explore social media variety by investigating the overlapped users between different social media networks. Two problems are discussed: (1) cross-network user modeling, where the scattered user behaviors are integrated for complete user modeling and personalized service development; (2) heterogeneous knowledge association, where the overlapped users serve as bridge to mine the cross-network knowledge association and applied in social media collaborative applications.

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a generalpurpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Tremendous amount of data is getting explored through IOT (Internet of Things) from variety of sources such as sensor network, social media feed, internet applications, called as Big Data. Big Data cannot be handled by conventional tools and techniques. Social networks are becoming dominant in communications over internet. The Big Data mining is essential in order to extract value from massive amount of data which could give better insights using efficient techniques. Association Rule mining and frequent itemset mining are popular techniques for data mining which needs entire dataset into main memory for processing, but large datasets do not fit into main memory. To overcome this limitation MapReduce is used for parallel processing of Big Data having features such as high scalability and robustness which helps to handle problem of large datasets. Proposed novel method, ClustBigFIM works on MapReduce framework for mining large datasets; ClustBigFIM is modified BigFIM algorithm providing scalability and speed in order to extract meaningful information from large datasets in the form of associations, emerging patterns, sequential patterns, correlations and other significant data mining tasks which gives better insight to make effective business decisions in competitive environment using faster and efficient parallel processing platform.

How does one start a new business online without thinking about its profit? Numbers of retailers with limited research, or asking enough details and questions, to make sure their product is something that is more likely to be sold and if does one considers enough storage to cater each customer needs. Why do facebook and amazon require Big Data in their business online? Right now, aspiring entrepreneurs are planning their paths to a more productive business using social commerce and taking it to the next level. It's a journey that requires a lot of hard work, and many people end up failing, but the business that is taking risks will end up reaping the cream of their crops. In this paper, we discuss the process of improving the production of an online business using big data in social media to increase sales and operating margin. That retailer, using Big Data to its current potential could gain a competitive edge to possibly gain opportunities for business and customers. Thus, we identify the advantages of using big data in social commerce.We also include the Java-based programming framework (Hadoop) that supports the processing and storage of a larger data set in a distributed computing environment. Naming it as next-generation enterprise data architecture that links the systems business transactions and business intelligence.