Predictive capacities of social media in the financial market: ARX-GARCH model

Twitter Archives and the Challenges of "Big Social Data" for Media and Communication Research

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.

Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the ‘Big Data’ paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze — and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing ‘scholarly divide’ between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.

Well beyond Internet Studies itself, but arguably led by it to a considerable extent, there has been a turn towards computational methods in the study of social and communicative phenomena at large scale. This “computational turn” has commonly been described as a turn towards “big data” or, more specifically, towards “big social data,” and it continues to drive the development of new research methodologies, approaches, and tools. Internet Studies has been an advocate of “big data” approaches, because the field connects several core disciplines that use “big data” methods – media, communication and cultural studies, the social sciences, and computer science. Equally, the major objects of research in Internet Studies – including platforms, search engines, mobile apps and devices, and Internet technologies and networks themselves – are key sources of “big data” on user interests, attitudes, and activities. Proponents of such approaches suggest that it is becoming possible to “study society with the Internet,” while others ask critical questions about which observations are privileged and which are discounted as the logic of “big data” influences research agendas. The early development and application of “big social data” research methods in Internet Studies, as well as critical interrogations of such approaches, focused especially on research into Twitter as a global social media platform. This is largely due to Twitter’s (initially) highly accessible application programming interface (API), which enabled the development of powerful research methods and the promise of large, sometimes real-time, datasets tracing patterns of user activity around specific themes and topics on the platform, as well as, by proxy, in wider society. Twitter’s tightening of API access serves as a reminder of the precarious nature of “big social data” research drawing on proprietary datasets, just as concerns about the use of social media data for the social profiling of individual users raise questions about research ethics and user privacy. The growing body of “big data” research drawing on Twitter as a data source has paradoxically also underlined the many limitations and blind spots of such approaches, as researchers drawing on publicly available API data struggle to place their findings in the context of a platform whose overall global shape is shrouded in considerably more mystery, due to Twitter, Inc.’s interest in keeping aspects of the platform and its user community commercial-in-confidence. The increased work in this field also highlights shortcomings in research training and publishing models, which need to be addressed to further develop “big social data” research. This chapter outlines the current state of the art in computationally driven Twitter research, using platform-specific research as a case study for the computational turn in Internet Studies. It will consider the opportunities and challenges inherent in this shift toward more data-driven research and outline the key needs for the discipline which have emerged to date. Even as Twitter’s own fortunes fluctuate, the experiences made in this branch of Internet Studies stand as a guide for broader developments in our field.

The objective of the paper is to reflect on the affordances of different techniques for making Twitter collections and to suggest the use of a random sampling technique, made possible by Twitter’s Streaming API (Application Programming Interface), for baselining, scoping, and contextualising practices and issues. It discusses this technique by analysing a one per cent sample of all tweets posted during a 24-hour period and introducing a number of analytical directions considered useful for qualifying some of the core elements of the platform, in particular hashtags. To situate the proposal, the report first discusses how platforms propose particular affordances but leave considerable margins for the emergence of a wide variety of practices. This argument is then related to the question of how medium and sampling technique are intrinsically connected. Background Social media platforms present numerous challenges to empirical research, making it different from researching cases in offline environments, but also different from studying the “open” Web. Because of the limited access possibilities and the sheer size of platforms like Facebook or Twitter, the question of delimitation, i.e. the selection of subsets to analyse, is particularly relevant. Whilst sampling techniques have been thoroughly discussed in the context of social science research, sampling procedures in the context of social media analysis are far from being fully understood. Even for Twitter, a platform having received considerable attention from empirical researchers due to its relative openness to data collection, methodology is largely emergent. In particular the question of how smaller collections relate to the entirety of activities of the platform is quite unclear. Recent work comparing case based studies to gain a broader picture and the development of graph theoretical methods for sampling are certainly steps in the right direction, but it seems that truly large-scale Twitter studies are limited to computer science departments, where epistemic orientation can differ considerably from work done in the humanities and social sciences.

IN MY judgment," maintained William McChesney Martin, Jr., Chairman of the Board of Governors, Federal Reserve System, "we can never under the present margin regulations have the same result that occurred in terms of a financial crash in 1929 through undermargined accounts, low margins resulting in a financial debacle."' A widely used elementary economics textbook claimed, "Margin requirements have exercised an important restrictive influence on security speculation, as contrasted with the uncontrolled period of the late 1920's... Nearly everyone agrees that Federal Reserve margin requirements exercise a healthy restraint on speculative stock purchases in a boom" (Bach, 1963, p. 116). But what evidence is there that margin requirements have the effects claimed? At the December meetings of the American Economic Association, George J. Stigler, in the Presidential address, called on economists to test their assumptions that state regulation of private activity has indeed been successful (Stigler, 1965). This article brings together some data on the efficacy of margin requirements. While the figures can only be considered preliminary, and more data should be collected to test margin requirements, the data that are available and presented here indicate that not one of the aims of the legislation establishing margin requirements has been accomplished.2

Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse. This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators. This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena.

Introducing more speculators into the market for a given commodity leads to improved risk sharing but can also change the informational content of prices. This inflicts an externality on those traders already in the market, whose ability to make inferences based on current prices will be aff ected. In some cases, the externality is negative: the entry of new s peculators lowers the informativeness of the price to existing trader s. The net result can be one of price destabilization and welfare red uction. This is true even when all agents are rational, risk-averse c ompetitors who make the best possible use of their available informat ion. Copyright 1987 by University of Chicago Press.

BackgroundLittle cigar and cigarillo use is becoming more prevalent in the United States and elsewhere, with implications for public health. As little cigar and cigarillo use grows in popularity, big social media data (eg, Instagram, Google Web Search, Twitter) can be used to capture and document the context in which individuals use, and are marketed, these tobacco products. Big social media data may allow people to organically demonstrate how and why they use little cigars and cigarillos, unprimed by a researcher, without instrument bias and at low costs.ObjectiveThis study characterized Swisher (the most popular brand of cigars in the United States, controlling over 75% of the market share) little cigar- and cigarillo-related posts on Instagram to inform the design of tobacco education campaigns and the development of future tobacco control efforts, and to demonstrate the utility in using big social media data in understanding health behaviors.MethodsWe collected images from Instagram, an image-based social media app allowing users to capture, customize, and post photos on the Internet with over 400 million active users. Inclusion criteria for this study consisted of an Instagram post with the hashtag “#swisher”. We established rules for coding themes of images.ResultsOf 1967 images collected, 486 (24.71%) were marijuana related, 348 (17.69%) were of tobacco products or promotional material, 324 (16.47%) showed individuals smoking, 225 (11.44%) were memes, and 584 (29.69%) were classified as other (eg, selfies, food, sexually explicit images). Of the marijuana-related images, 157/486 (32.3%) contained a Swisher wrapper, indicating that a Swisher product was used in blunt making, which involves hollowing out a cigar and refilling it with marijuana.ConclusionsImages from Instagram may be used to complement and extend the study of health behaviors including tobacco use. Images may be as valuable as, or more valuable than, words from other social media platforms alone. Posts on Instagram showing Swisher products, including blunt making, could add to the normalization of little cigar and cigarillo use and is an area of future research. Tobacco control researchers should design social media campaigns to combat smoking imagery found on popular sites such as Instagram.

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social 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.

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the government. People are open to sharing opinions, views, and ideas on any topic in different formats out loud. This creates the opportunity to make the “Big Social Data” handy by implementing machine learning approaches and social data analytics. This study offers an overview of recent works in social media, data science, and machine learning to gain a wide perspective on social media big data analytics. We explain why social media data are significant elements of the improved data-driven decision-making process. We propose and build the “Sunflower Model of Big Data” to define big data and bring it up to date with technology by combining 5 V's and 10 Bigs. We discover the top ten social data analytics to work in the domain of social media platforms. A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work. “Text Analytics” is the most used analytics in social data analysis to date. We create a taxonomy on social media analytics to meet the need and provide a clear understanding. Tools, techniques, and supporting data type are also discussed in this research work. As a result, researchers will have an easier time deciding which social data analytics would best suit their needs.

Justice on the Digitized Field: Analyzing Online Responses to Technology-Facilitated Informal Justice through Social Network Analysis

Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.

Social media has become very popular over the past decade. There are millions of users across the world sharing information with each other instantaneously through several social media platforms. With these many users sharing huge volumes of data analysis of social media data has become a prominent area of research. Recent studies on the use of data from social media platforms such as Twitter for predicting political elections have raised many questions as well as created the interest in using Twitter data for predictive analysis. The overarching objective of this paper is to study the capability of Twitter data as an ex-ante indicator of event outcomes by modeling the momentum of political campaigns. Three indicators with predictive capability are proposed as measures of momentum of political campaigns. An asset price model is adapted to model momentum of candidates. Empirical validation is provided based on Twitter data from the 2014 US midterm election and the 2016 Presidential primary elections. Our results support the argument that data from social media can be considered as a reliable predictor of events in political campaigns.