Social Media Analytics in Food Innovation and Production: a Review

Until recently social media and social media analytics (SMA) were basically used only for communication and marketing purposes. However, thanks to advances in digital technologies and big data analytics, potential applications of SMA extend now to production processes and overall business management. As a result, SMA has become an important tool for gaining and sustaining competitive advantage across various sectors, industries and end-markets. Yet, the food industry still lags behind when it comes to the use of digital technologies and advanced data analytics. A part of the explanation lies in the limited knowledge of potential applications of SMA in food innovation and production. The aim of this paper is to provide a review of literature on possible uses of SMA in the food industry sector and to discuss both the benefits, risks, and limitations of SMA in food innovation and production. Based on the literature review, it is concluded that mining social media data for insights can create significant business value for the food industry enterprises and food service sector organizations. On the other hand, many proposals for using SMA in the food domain still await direct experimental tests. More research and insights concerning risks and limitations of SMA in the food sector would be also needed. The issue of responsible data analytics as part of Corporate Digital Responsibility and Corporate Social Responsibility of enterprises using social media data for food innovation and production also requires a greater attention

Exploring the integration of artificial intelligence (AI) and augmented reality (AR) in maritime medicine

This narrative literature review has analyzed the integration of artificial intelligence (AI) and augmented reality (AR) in the field of maritime medicine. A comprehensive search was conducted in academic databases using relevant search terms, resulting in the identification of 257 records. After screening for relevance and quality, a final review was conducted on 17 papers. This review highlights the potential applications and benefits of AI and AR in enhancing medical practices and safety measures for seafarers. The integration of AI and AR technologies in maritime medicine shows promise in providing real-time medical assistance, remote consultations, augmented training, and improved diagnostic capabilities. Additionally, AI-driven predictive models can aid in early detection of health issues and support proactive health management onboard ships. Challenges related to data privacy, connectivity at sea, and the need for regulatory frameworks are also discussed. The data analysis reported in this review contributes to a better understanding of the current state and future potential of AI and AR in maritime medicine and provide insights into opportunities for further research and implementation in the maritime industry

Identifying methods for monitoring foodborne illness: review of existing public health surveillance techniques

Background: Traditional methods of monitoring foodborne illness are associated with problems of untimeliness and underreporting. In recent years, alternative data sources such as social media data have been used to monitor the incidence of disease in the population (infodemiology and infoveillance). These data sources prove timelier than traditional general practitioner data, they can help to fill the gaps in the reporting process, and they often include additional metadata that is useful for supplementary research. Objective: The aim of the study was to identify and formally analyze research papers using consumer-generated data, such as social media data or restaurant reviews, to quantify a disease or public health ailment. Studies of this nature are scarce within the food safety domain, therefore identification and understanding of transferrable methods in other health-related fields are of particular interest. Methods: Structured scoping methods were used to identify and analyze primary research papers using consumer-generated data for disease or public health surveillance. The title, abstract, and keyword fields of 5 databases were searched using predetermined search terms. A total of 5239 papers matched the search criteria, of which 145 were taken to full-text review—62 papers were deemed relevant and were subjected to data characterization and thematic analysis. Results: The majority of studies (40/62, 65%) focused on the surveillance of influenza-like illness. Only 10 studies (16%) used consumer-generated data to monitor outbreaks of foodborne illness. Twitter data (58/62, 94%) and Yelp reviews (3/62, 5%) were the most commonly used data sources. Studies reporting high correlations against baseline statistics used advanced statistical and computational approaches to calculate the incidence of disease. These include classification and regression approaches, clustering approaches, and lexicon-based approaches. Although they are computationally intensive due to the requirement of training data, studies using classification approaches reported the best performance. Conclusions: By analyzing studies in digital epidemiology, computer science, and public health, this paper has identified and analyzed methods of disease monitoring that can be transferred to foodborne disease surveillance. These methods fall into 4 main categories: basic approach, classification and regression, clustering approaches, and lexicon-based approaches. Although studies using a basic approach to calculate disease incidence generally report good performance against baseline measures, they are sensitive to chatter generated by media reports. More computationally advanced approaches are required to filter spurious messages and protect predictive systems against false alarms. Research using consumer-generated data for monitoring influenza-like illness is expansive; however, research regarding the use of restaurant reviews and social media data in the context of food safety is limited. Considering the advantages reported in this review, methods using consumer-generated data for foodborne disease surveillance warrant further investment

Selecting the Best Subset of Features Using a Game-theoretic Approach: Applications in Information Systems

The curse of dimensionality is a major issue in datasets related to Information Systems (IS) because of the volume of data coming from smartphones, cameras, wireless sensory networks, social media, Internet search, etc. For such datasets applying a proper feature selection method can boost the performance of prediction or classification methods. While there are many feature selection techniques that can be used in the IS domain, the performance of them is problem-specific and they may not perform well on many datasets. Therefore, in this study, we address this issue by developing a novel method that employs ideas from the field of game theory. A computational study on real-life classification IS datasets shows that our proposed method outperforms or do as well as other benchmarks

Mobile Sensing, Simulation and Machine-learning Techniques: Improving Observations in Public Health

Entering an era where mobile phones equipped with numerous sensors have become an integral part of our lives and wearable devices such as activity trackers are very popular, studying and analyzing the data collected by these devices can give insights to the researchers and policy makers about the ongoing illnesses, outbreaks and public health in general. In this regard, new machine learning techniques can be utilized for population screening, informing centers of disease control and prevention of potential threats and outbreaks. Big data streams if not present, will limit investigating the feasibility of such new techniques in this domain. To overcome this shortcoming, simulation models even if grounded by small-size data can represent a simple platform of the more complicated systems and then be utilized as safe and still precise environments for generating synthetic ground truth big data. The objective of this thesis is to use an agent-based model (ABM) which depicts a city consisting of restaurants, consumers, and an inspector, to investigate the practicability of using smartphones data in the machine-learning component of Hidden Markov Model trained by synthetic ground-truth data generated by the ABM model to detect food-borne related outbreaks and inform the inspector about them. To this end, we also compared the results of such arrangement with traditional outbreak detection methods. We examine this method in different formations and scenarios. As another contribution, we analyzed smart phone data collected through a real world experiment where the participants were using an application Ethica Data on their phones named. This application as the first platform turning smartphones into micro research labs allows passive sensor monitoring and sending over context-dependent surveys. The collected data was later analyzed to get insights into the participants' food consumption patterns. Our results indicate that Hidden Markov Models supplied with smart phone data provide accurate systems for foodborne outbreak detection. The results also support the applicability of smart phone data to obtain information about foodborne diseases. The results also suggest that there are some limitations in using Hidden Markov Models to detect the exact source of outbreaks

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies

Reducing the Risk: Psychological and Technological Approaches for Improving Handwashing Practices in the Foodservice Industry

As Americans are spending greater portions of their dollar on food consumed outside the home, the foodservice industry plays more of an integral part of daily existence compared to previous generations. Given the numerous annual foodborne illness outbreaks that threaten human lives while undermining confidence in the food supply, food safety is a pertinent issue for industry stakeholders, government regulators, and consumers. Food worker handwashing reduces the risk of foodborne illness transmission, yet compliance with this simple behavior is a complex problem. This dissertation addresses, predominantly, the issue of sub-optimal handwashing practices through applying psychology and technology, including wearable computers and a video game. Chapter one discusses prior efforts to improve handwashing compliance, while providing a theoretical framework to guide industry professionals through strategies that consider the potentially negative psychological effects of interventions on employees. Chapter two highlights handwashing practices of early childhood center food handlers. While average compliance was 22%, strict adherence to the guidelines would have required 12 minutes/hour devoted to handwashing. Chapter three explores handwashing in relation to organizational climate factors; managerial commitment was the only significant predictor of handwashing. Chapter four shows wearable technology-based training is preferred by food handlers. Chapter five indicates how participants who viewed strictly video-based training were four times as likely to wash hands compared to participants trained with smart glasses. Chapter six highlights the efficiency of handwashing training with smart glasses. Chapter seven includes the design and development of a video game played while washing hands. Perceptions of the device were only slightly positive, showing the need for either improved reward mechanisms or alternative strategies to motivate handwashing. Chapter eight evaluates the relationship between risk classification of foodservice establishments and food safety violation rates. High priority facilities had significantly higher food safety violation rates compared to medium and low priority facilities. In looking to the future of foodservice, many jobs are highly susceptible to automation; emotional intelligence may translate to greater job security in the coming years. Chapter nine evaluated perceptions of job insecurity rendered by automation in relation to emotional intelligence. There was no correlation between the two variables

How Fair Is IS Research?

While both information systems and machine learning are not neutral, the identification of discrimination is more difficult if a system learns from data and discrimination can be introduced at several stages. Therefore, this article investigates if IS Research has taken up with this topic. A literature analysis is conducted and its discussion shows that technology, organization, and human aspects have to be considered, making it a topic not only for data scientist or computer scientist, but for information systems researchers as well