Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.
With the proliferation of social sensing, large amounts of observation are contributed by people or devices. However, these observations contain disinformation. Disinformation can propagate across online social networks at a relatively low cost, but result in a series of major problems in our society. In this survey, we provide a comprehensive overview of disinformation and truth discovery in social sensing under a unified perspective, including basic concepts and the taxonomy of existing methodologies. Furthermore, we summarize the mechanism of disinformation from four different perspectives (i.e., text only, text with image/multi-modal, text with propagation, and fusion models). In addition, we review existing solutions based on these requirements and compare their pros and cons and give a sort of guide to usage based on a detailed lesson learned. To facilitate future studies in this field, we summarize related publicly accessible real-world data sets and open source codes. Last but the most important, we emphasize potential future research topics and challenges in this domain through a deep analysis of most recent methods.
Although ransomware has been around since the early days of personal computers, its sophistication and aggression have increased substantially over the years. Ransomware, as a type of malware to extort ransom payments from victims, has evolved to deliver payloads in different attack vectors and on multiple platforms, and creating repeated disruptions and financial loss to many victims. Many studies have performed ransomware analysis and/or presented detection, defense, or prevention techniques for ransomware. However, because the ransomware landscape has evolved aggressively, many of those studies have become less relevant or even outdated. Previous surveys on anti-ransomware studies have compared the methods and results of the studies they surveyed, but none of those surveys has attempted to critique on the internal or external validity of those studies. In this survey, we first examined the up-to-date concept of ransomware, and listed the inadequacies in current ransomware research. We then proposed a set of unified metrics to evaluate published studies on ransomware mitigation, and applied the metrics to 118 such studies to comprehensively compare and contrast their pros and cons, with the attempt to evaluate their relative strengths and weaknesses. Finally, we forecast the future trends of ransomware evolution, and propose future research directions.
Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field.
It is challenging to provide users with a haptic weight sensation of virtual objects in VR since current consumer VR controllers and software-based approaches such as pseudo-haptics cannot render appropriate haptic stimuli. To overcome these limitations, we developed a haptic VR controller named Triggermuscle that adjusts its trigger resistance according to the weight of a virtual object. Therefore, users need to adapt their index finger force to grab objects of different virtual weights. Dynamic and continuous adjustment is enabled by a spring mechanism inside the casing of an HTC Vive controller. In two user studies, we explored the effect on weight perception and found large differences between participants for sensing change in trigger resistance and thus for discriminating virtual weights. The variations were easily distinguished and associated with weight by some participants while others did not notice them at all. We discuss possible limitations, confounding factors, how to overcome them in future research and the pros and cons of this novel technology.
The limited supply of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) raises the question of targeted vaccination. Many countries have opted to vaccinate older and more sensitive hosts first to minimize the disease burden. However, what are the evolutionary consequences of targeted vaccination? We clarify the consequences of different vaccination strategies through the analysis of the speed of viral adaptation measured as the rate of change of the frequency of a vaccine-adapted variant. We show that such a variant is expected to spread faster if vaccination targets individuals who are likely to be involved in a higher number of contacts. We also discuss the pros and cons of dose-sparing strategies. Because delaying the second dose increases the proportion of the population vaccinated with a single dose, this strategy can both speed up the spread of the vaccine-adapted variant and reduce the cumulative number of deaths. Hence, strategies that are most effective at slowing viral adaptation may not always be epidemiologically optimal. A careful assessment of both the epidemiological and evolutionary consequences of alternative vaccination strategies is required to determine which individuals should be vaccinated first.
Enterprise risk management (ERM) is significant in running a company. ISO 31000 is one of the ERM types that are familiar. However, there are still pros and cons of ISO 31000. Thus, this study aimed to find out the responses of the Indonesian fintech lending companies managements in implementing ISO 31000. Specifically, this study tried to identify the problems in implementing ISO 31000:2018 to be implemented as ERM. Besides, it also discussed the benefits of the ISO 31000:2018 implementation from the companies' management's perspectives. The data were collected through questionnaires and interviews. The questionnaire results were quantified and interpreted in percentage, while the interview results were analyzed qualitatively using the interactive data analysis method. The study results showed that most of the companies' management believed that they had no significant problems implementing ISO 31000:2018. In addition, they felt that implementing ISO 31000:2018 as ERM gave many benefits in running the companies. The study's findings were discussed by connecting them with the current theories and empirical reviews. However, since the study was done qualitatively, a further study that involves quantitative study to measure the effectiveness of ISO 31000 empirically is needed to support the results of this study.
The case will familiarize students to TEARS model and No TEARS approach for brand endorser selection. It will enable the students to understand the extent of influencer impact on the brand. It will make students to realistically assess the pros and cons of ambassador marketing using celebrities. It will also enable the student to devise brand strategies to mitigate the risk associated with ambassador-based marketing.
The case explains the strategic challenge the brand faces in ambassador marketing due to the uncontrollable personal crisis of the celebrity. Brand ambassador is an integral element of the brand persona and is appointed to boost the brand’s unique proposition and sales. The selection of the brand ambassador is a strategic decision with direct implication on the brand equity. A strong celebrity–brand congruence is ideal to establish credibility, but it can backfire if anything negative occurs on either side. This case evaluates the crisis faced by Fortune oil which has been positioned as “the heart healthy oil” when its celebrity ambassador suffers heart attack. In the backdrop of the case, the students can analyse brand strategies with respect to ambassador marketing, TEARS model with No TEARS approach for endorser selection and endorser-related credibility risk management.
Complexity academic level
MBA BBA PG/Graduation in Marketing/Advertising.
Teaching Notes are available for educators only.
CSS 8: Marketing.