Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

ELECTRONIC WORD-OF-MOUTH: APPLICATIONS IN PRODUCT RECOMMENDATION AND CRISIS INFORMATION DISSEMINATION

Ph.DDOCTOR OF PHILOSOPH

Dynamic data placement and discovery in wide-area networks

The workloads of online services and applications such as social networks, sensor data platforms and web search engines have become increasingly global and dynamic, setting new challenges to providing users with low latency access to data. To achieve this, these services typically leverage a multi-site wide-area networked infrastructure. Data access latency in such an infrastructure depends on the network paths between users and data, which is determined by the data placement and discovery strategies. Current strategies are static, which offer low latencies upon deployment but worse performance under a dynamic workload. We propose dynamic data placement and discovery strategies for wide-area networked infrastructures, which adapt to the data access workload. We achieve this with data activity correlation (DAC), an application-agnostic approach for determining the correlations between data items based on access pattern similarities. By dynamically clustering data according to DAC, network traffic in clusters is kept local. We utilise DAC as a key component in reducing access latencies for two application scenarios, emphasising different aspects of the problem: The first scenario assumes the fixed placement of data at sites, and thus focusses on data discovery. This is the case for a global sensor discovery platform, which aims to provide low latency discovery of sensor metadata. We present a self-organising hierarchical infrastructure consisting of multiple DAC clusters, maintained with an online and distributed split-and-merge algorithm. This reduces the number of sites visited, and thus latency, during discovery for a variety of workloads. The second scenario focusses on data placement. This is the case for global online services that leverage a multi-data centre deployment to provide users with low latency access to data. We present a geo-dynamic partitioning middleware, which maintains DAC clusters with an online elastic partition algorithm. It supports the geo-aware placement of partitions across data centres according to the workload. This provides globally distributed users with low latency access to data for static and dynamic workloads.Open Acces

Three essays on malicious consumer deviance: The creation, dissemination, and elimination of misleading information

With the explosion of social media, consumers are gaining control in social reach and can utilize online platforms to create and share misleading information when doing so helps to meet an end. This dissertation, consisting of three separate essays, represents an attempt to address how misleading information is created, how it is disseminated, and how it can be eliminated. Essay One (Chapter 2) uses a mixed-method approach to explore the Dark Triad, proactivity, and vigilantism in driving self-created misleading information sharing. Additionally, this essay introduces a dual-process model of inoculation theory to the marketing and consumer literature that shows how consumers autoinoculate when building justification to engage in malicious behavior. This process includes both automatic and analytical components that initiate a Negative Cascade. Without a larger number of posts, these initial messages may be overlooked. However, herd inoculation can develop when a message begins to sway larger groups. Essay Two (Chapter 3) determines that authentic messages from the original poster are most believable and most likely to initiate a Negative Cascade. This confirmation through mere exposure can then initiate herd inoculation as it flows to other consumers and develops further credibility. The implicit bystander effect is active when in the presence of larger groups. Findings suggest herd inoculation may go unbroken since posters exposed to a positive counter-cascade are less likely to both participate in a forum and post positive messages. Essay Three (Chapter 4) shows that when a consumer shares a message that develops into a Negative Cascade, additional effort is required to halt the consumer herd inoculation. The studies uncover the need for an overt response from the original poster to stop future sharing of misleading information and the role of brand-enacted quarantines in the prevention of the autoinoculation of consumer vigilantes. This dissertation shows how one message can become a much bigger problem for a brand when misinformation spreads. Insights within the dissertation provide numerous outlets for future research and numerous tools and recommendations for both academics and practitioners that hope to understand how misleading information is created, disseminated, and can be eliminated

A Multidisciplinary Perspective of Big Data in Management Research

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in management studies and develop an integrated framework to link the multiple streams of research in fields of organisation, operations, marketing, information management and other relevant areas. Our analysis uncovers a growing awareness of big data's business values and managerial changes led by data-driven approach. Stemming from the review is the suggestion for research that both structured and unstructured big data should be harnessed to advance understanding of big data value in informing organisational decisions and enhancing firm competitiveness. To discover the full value, firms need to formulate and implement a data-driven strategy. In light of these, the study identifies and outlines the implications and directions for future research

Beyond mobile advertising : an empirical investigation of customer engagement and empowerment with mobile marketing communication campaigns

The importance of customer engagement to sustain and foster business growth in an interactive environment has been recognised in the practitioner literature. It has also been observed that engaged customers become empowered in a given marketing communication environment. Yet, there has been very little, if any, academic enquiry examining these concepts within the mobile commutations context. This is surprising given that we are live in an increasingly mobile technology dominated world. Thus, the aim of this research is to examine customer engagement behaviour and its relationship to customer empowerment in the context of mobile communication. A conceptual model is built on the foundations of the technology acceptance model (TAM). This model seeks to explain the level of engagement and empowerment of customers in mobile marketing campaigns with subjective norms, information seeking, perceived ease of use and perceived usefulness as antecedents. The inquiry extends to examining the impact of moderating factors that influence customer engagement and empowerment along with behavioural intention as a consequence.Following Churchill (1979), a scale to measure engagement was developed. Given the positivist foundations of this study, an online questionnaire was used to collect data. Respondents were recruited from several popular electronic forums in Saudi Arabia. Following data collection, covariance based Structural Equation Modelling was employed in the analysis.The study makes a contribution both on a theoretical level and at a practical level. On a theoretical level, a new scale is developed to measure customer engagement. This will provide a basic understanding of customer behaviour in mobile marketing communication. The relationship between customer engagement and customer empowerment was significant. Subjective norms and information seeking were significant to customer empowerment, while only subjective norms were significant to customer engagement. Perceived usefulness was significant to customer engagement and customer empowerment, while perceived ease of use was insignificant to both of them. In addition, behavioural intention was significant to customer empowerment. On a practical level, the developed scale will help to improve customers’ relationships with businesses; as marketers are now able to enhance engagement by providing an outlet for social interaction, for example. Furthermore, a better understanding of customers’ behaviour will help marketing professionals to better segment and target the appropriate customers to enhance their loyalty