A survey of big data in social media using data mining techniques

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.

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.

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big Data analytics and Machine Learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the “Four Vs” of Big Data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big Data and machine learning in driving innovation and decision-making in the digital era.

Social Media (SM) data are growing, and SM is becoming an acceptable part of daily life for billions of people around the world. Extracting information from Social Networking Sites (SNS) can provide great challenges as well as opportunities. Using SM data beyond day-to-day communication can provide additional values. There is much research and many products that are dedicated to take SNS beyond communication channels. In our research, we are going beyond specific tools inherent to the SM tools, such as Hashtag mentions and Like counts. Instead it will use text-based modeling, data mining techniques, natural process language, machine language, etc. to understand SM content to produce numeric ratings. The final contribution of this research is building a SM users' rating model for an event using SM data. At this point of our research, we are laying out a road map.

The article contains results of a study of social media data (SMD) which, being distinct from conventional data by their origin, require special methods for collection, processing and analysis. As shown by a literature review, in spite of great many research publications devoted to social media research and big data analysis, the SMD potential as a big data component still remains inadequately explored. Two approaches to research and analysis of SMD were highlighted in course of the study, in which SMD are addressed as an object of Internet statistics and an object of big data. When SMD are explored as an object of Internet statistics, collection of anonymized data is performed using the services that have network protocols for collection and analysis of data on social media customers using statistical methods. When SMD are explored as an object of big data, the collection is performed mostly by artificial intellect, whereas the storage and processing is operated by databases designed for large scopes of data and software with statistical data processing applications. The social media most popular with users in 2020 were identified in the study. Statistical indicators for assessment of users’ feedback, available now for statistical assessments of social media communities, are given. The study revealed several problems which solutions would require, apart from a multifaceted and complex approach to collection and processing, highly competent teams of specialists in various subject fields, including experts in computations, experts in machine learning and statisticians.

Social media is being employed to build support for social, economic, and political justice (Selander et al. 2016; Vaast et al. 2014) in movements such as occupy Wall Street, violence against women's movement and global sustainability movement. These movements have used social media in ways that goes beyond simple communications. As social media allows people to produce and share user generated content, they enable a certain set of affordances of these technologies (Bharati et al 2015; Bharati et al 2014). The social media affordances, available to both collective and individual actors, translate into capabilities afforded to social movements (Tufecki 2014). Research on connective action has examined the effects of digital action repertoires on interaction and engagement such as in the Tea Party and Occupy movements (Agarwal et al. 2014; Selander et al. 2016). Social media can also facilitate mobilization of movement and participation by new volunteers and oftentimes provides a transnational character by diffusing actions beyond the virtual (Van Laer and Van Aelst 2010). Conversation, an essential part of social movements, shapes “social life by altering individual and collective understandings, by creating and transforming social ties, by generating cultural materials that are then available for subsequent social interchange, and by establishing, obliterating, or shifting commitments on the part of participants&x201D; (Tilly 2002, p. 122). In a personal interaction that involves repeated organized interactions between individuals, typically, leads to shared values and trust. The role of social media technologies in furthering this conversation has to be studied and its' influence on social movement ascertained. A few scholars have started to investigate social media affordances and capabilities, especially focused on discourse, during contentious collective action. Still the research has been limited to studying mechanisms of participation, development of a sense of collective identity, creation of community, and framing of political discourse (Farrell 2012; Garrett 2006). Social media data on social movements can involve impersonal “like&x201D; and “share&x201D; to more engaged conversations. Twitter, Instagram, Facebook, and YouTube offer a wide communicative and discourse reach in networks with text, image, audio, and video data. This social media based big data, consisting mostly of unstructured data, comes in the form of social media posts, digital pictures and videos. The symposium will discuss how formal organizational structures and practices might be integrated with social media capabilities to reinforce and enhance social movement organizations as leaders in social change movements. It will explore the characteristics of social media discourse and assess the dynamics of social movements and, subsequent, impact on real-world protests. The symposium will also demonstrate how discourse analysis can be applied visually in order to understand communication patterns. The panel symposium will focus on theoretical and methodological challenges of social media analytics, big data and social movements. Panelists will engage the audience in an interactive discussion on: 1) Theoretical challenges: a. How and why are social movement recruitment and engagement mechanisms being impacted as a result of social media? b. How do we advance theory on social media and social movements when we are overwhelmed with social media based big data? c. What approach should we undertake if big data analysis contradicts most theories on social media and social movements? d. How do we address the issue of generalizability of social media and social movement research when data collection was limited to one social media platform, albeit involving big data? 2) Methodological challenges: a. What methodological approaches have worked in the analysis of social media, big data and social movements? b. How can discourse analysis be applied visually in order to understand communication patterns evident in social media-based big data? c. How can we employ social media analytics to investigate image and video data? d. What combinations of qualitative and quantitative methodologies be employed for big data and social media analytics in the context of social movements? e. What are the limitations of quantitative data analysis techniques, such as structural equation modeling, because of an extremely large sample size? f. What are the limitations of qualitative data analysis techniques as they become extremely labor intensive and, maybe even, impractical because of big data?

Fear of Missing Out (FoMO) and social media’s impact on daily-life and productivity at work: Do WhatsApp, Facebook, Instagram, and Snapchat Use Disorders mediate that association?

We explored the transformation of social media content into three radically different formats; a book, a triptych of photographs and film.Using interviews, we investigated users' responses to their remediated data.Our findings were used to develop a digital curation process to assist the development of different tools for social media analysis and display.Our paper establishes design implications to aid reflection on social media use through remediation. Increasingly our digital traces are providing new opportunities for self-reflection. In particular, social media (SM) data can be used to support self-reflection, but to what extent is this affected by the form in which SM data is presented? Here, we present three studies where we work with individuals to transform or remediate their SM data into a physical book, a photographic triptych and a film. We describe the editorial decisions that take place as part of the remediation process and show how the transformations allow users to reflect on their digital identity in new ways. We discuss our findings in terms of the application of Goffman's (1959) self-presentation theories to the SM context, showing that a fluid rather than bounded interpretation of our social media spaces may be appropriate. We argue that remediation can contribute to the understanding of digital self and consider the design implications for new SM systems designed to support self-reflection.

Escaping to nature during a pandemic: A natural experiment in Asian cities during the COVID-19 pandemic with big social media data

Compared to natural language processing research investigating suicide risk prediction with social media (SM) data, research utilizing data from clinical settings are scarce. However, the utility of models trained on SM data in text from clinical settings remains unclear. In addition, commonly used performance metrics do not directly translate to operational value in a real-world deployment. The objectives of this study were to evaluate the utility of SM-derived training data for suicide risk prediction in a clinical setting and to develop a metric of the clinical utility of automated triage of patient messages for suicide risk. Using clinical data, we developed a Bidirectional Encoder Representations from Transformers-based suicide risk detection model to identify messages indicating potential suicide risk. We used both annotated and unlabeled suicide-related SM posts for multi-stage transfer learning, leveraging customized contemporary learning rate schedules. We also developed a novel metric estimating predictive models' potential to reduce follow-up delays with patients in distress and used it to assess model utility. Multi-stage transfer learning from SM data outperformed baseline approaches by traditional classification performance metrics, improving performance from 0.734 to a best F1 score of 0.797. Using this approach for automated triage could reduce response times by 15 minutes per urgent message. Despite differences in data characteristics and distribution, publicly available SM data benefit clinical suicide risk prediction when used in conjunction with contemporary transfer learning techniques. Estimates of time saved due to automated triage indicate the potential for the practical impact of such models when deployed as part of established suicide prevention interventions. This work demonstrates a pathway for leveraging publicly available SM data toward improving risk assessment, paving the way for better clinical care and improved clinical outcomes.

The blooming of social media services makes them the attractive resources for publishing and seeking information (posting, reposting and searching tweets) as well as socializing and interacting with other users (following and messaging other users) in social media services. With the increasing number of users and the interacting frequency between users, tremendous user-generated contents are bursting out every day. Hence, users may face a overload of information. To address the above problem, in this paper, we present SocialRobot, a humanoid intelligent system that has been deployed in Sina Weibo. For its socialization characteristic, we naturally implement it as a virtual user in Sina Weibo. By following and interacting with SocialRobot, high-quality and user-interested content can be recommended to the users from the big social media data. And the need for searching specific information can be satisfied by chatting with the SocialRobot. A crowdsourcing evaluation shows the effective performance of the SocialRobot on resolving the information overload.

Purpose Information technologies are commonly used in architectural and urban design. The use of these technologies providing support at every stage of the design opens up different perspectives for designers and users. The aim of the study is to obtain user demands for green spaces of a specific district by mining data through social media and to detect the actual green spaces of the same district using applications developed for this purpose. User demands for design decisions and applications of green spaces and the current situation of the study area are evaluated. Design/Methodology/Approach The research is firstly realized through social media, and data obtained from Twitter is analysed in order to evaluate user demands for parks and green spaces of Ataşehir district in İstanbul City. Secondly, all green areas in the same district are detected by using digital maps. Two applications are specifically designed for this research; Tweet Grabber is used for user sentiment analysis on social media and Map Grabber is processed for extraction of green spaces via maps. The total area of the green spaces is compared with the desired area of open and green spaces per user. Findings The user demands and thoughts obtained in the study about the green spaces of the district are compatible with the actual situation of green spaces. It is observed that the users are mostly dissatisfied with the adequacy of green spaces. Designers, politicians, municipalities and all stakeholders can benefit from the obtained user expectations and feedback. Interpreting user demands by mining data through social media enables user participation in design decisions. This research method can be supportive and adaptive in related issues of design for the cities, enabling user participation in architectural and urban design. Research Limitations/Implications Parks and green spaces of Ataşehir district of İstanbul are taken as a case study. Twitter is chosen for mining of data in social media based on parameters such as keywords and location. Social/Practical Implications The impact and support of users in design decisions can be clearly demonstrated by advanced information technologies. Mining data through social media and developed applications will contribute to design decisions and policies for architectural and urban spaces. Originality/Value Tweet Grabber and Map Grabber applications are developed for this research in order to get text based and image based data. The research includes a unique case study for mining data through social media on a specific design issue and target location.

Real applications like crisis management require the real time awareness of critical situations. However, the services using traditional methods like phone calls can be easily delayed due to busy lines, transfer delays or limited communication ability in disaster areas. Existing social event analysis solutions enhanced the situation awareness of systems. Unfortunately, they cannot recognize the complex migrating social events that are first observed in social media at a specific time, place and state, but have further moved in space and time, which may affect the system comprehension. While the discussion on events appears in microblogs, their movement over different contexts is unavoidable. So far, the problem of migrating social event analysis from big media is not well investigated yet. To address this issue, we propose a novel framework to monitor and deliver the migrating events in big social media data, which fully exploits the social media information over multiple attributes and their inherent interactions among events. Specifically, we first propose a Concept TF/IDF model to capture the content that is constrained by the time and location of media without costly learning process. Then, we construct a novel Maximal User Influence Graph (MUIG) to extract the social interactions. With MUIG, the event migrations over space and time are well identified. Finally, we design efficient query strategies over Apache Spark for recommending events in real time. Extensive tests over big media are conducted to prove the high effectiveness and efficiency of our approach.

Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com). In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of those social networking media such as Hotel, Villa, SPA, restaurants, travel agents, airlines, media, tourist attraction management has a good rating result, and it has represented that the image of Bali tourism has a good rank in the eyes of local and international tourists. This data was also strengthened by the existence of empirical studies where there were the data from a hundred tourists came to Bali both locally and internationally has filled out the questionnaire about Bali in order to obtain maximum results on the image of Bali tourism. The results showed that, social networking media were effectively used in this study. Many companies in Bali used facebook and twitter. In addition, Bali Tourism image placed in positive rank on both of social networking sites, and it was gained by the results of questionnaires spread towards the visitor.

Predicting diffusions on big social media data using natural language processing (NLP) and social network analysis (SNA) techniques is an emerging research domain. To predict diffusions of novel topics, previous studies focus on predicting the diffusions on cross-topic-observed diffusions (the diffusions between the source and target user of the diffusion are not observed for the topic to be predicted, but still observed for other topics). However, in real world social network, many diffusions to be predicted are actually unobserved. For example, the diffusions may be unseen (the diffusions between the source and target user of the diffusion are not observed in training data), or even with silence users (one or both of the users of the diffusion never participate a diffusion before). In this paper, we generalize the diffusion prediction on novel topic problem to predict both cross-topic-observed and unobserved diffusions, which is very challenging because of lacking previous diffusion records. We design a learning-based framework to solve the problem. Leveraging NLP and SNA techniques to deal with such Big Data, we exploit the latent semantic derived from diverse information sources (e.g., user, topic, user-topic, and topological), and utilize the idea that "users with the same attribute value tend to have similar behavior for similar topics", to extract features for prediction. Our framework is evaluated on real-world microblog data, and the experiments show that we can achieve 73% AUC in this difficult prediction task. Our dataset is also publicly available at http://mslab.csie.ntu.edu.tw/~tim/ase_big_data_2015.zip.