The Current Status of Social Risks on Educational Systems. An Analysis Through Social Media

Este trabajo ha sido publicado en las actas del citado congreso, y revisados los documentos donde ha sido publicado, no se muestran impedimentos legales para que pueda ser publicado el documento.Social Risk in education such as bullying, are usually invisible to teachers and parents, at all educational levels. However, these risks remain a reality everywhere in the world, turning into a problem that is rapidly globalizing due to the widespread access to the Internet. The Internet has permeated our entire society and is now present in almost every activity. The education and most aspects associated with it, such as Social Risks, are not exempt of this new form of communication within our society. This has led to a significant increase in damage Social Risks can exhort on the victims, due to several causes such as their capacity for dissemination, repetition and virality; greater anonymity of aggressors and the chance for more people joining them; continuity over time even when after school hours; display of intimacy before an endless crowd of people; ease of permanent control through geolocation, control of online statuses and connections; and even the risk of easily impersonating a victim. The first step to prevent these issues is to carry out a study on the current state of Social Risks. An updated snapshot would allow to draw up action plans based on reliable data and develop countermeasures to minimize the damage caused by current Social Risks to minors. The objective of this work is to conduct a study on unsolicited data obtained from Social Media on three of the most prominent Social Risks of our society, namely Bullying, Addictions and Xenophobia within the field of education, with the aim of obtaining an updated snapshot of their current status. The study was carried out during the second semester of 2017 and the first semester of 2018, quantifying the presence and emotion of said risks in Social Media, determining the most relevant terms, as well as the most used communication channels

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Residential development choices and consequences: Urban land cover change, perceptions and value of alternative subdivision designs, and the benefits of protected ecosystem services

Municipal officials are often faced with difficult decisions about land uses in and around city boundaries. Urban expansion often causes negative environmental impacts, but there are designs for development which can mitigate some of these effects at the site scale. This dissertation examines urban land cover change in four Iowa cities, examines familiarity with and value for conservation subdivision (CSD) and low-impact design (LID) features, and explores how conservation subdivision design can protect urban ecosystem services. Public datasets and GIS software were used to assess land cover change for four municipalities in Iowa as a framework for predicting land cover change impacts by determining where loss of natural areas has occurred and where future losses are most likely to happen. Urban land cover increased by 28-80% in the four communities examined, primarily involving transitions from grassland and cropland. Losses of mature forest areas occurred (348 to 1335 ha) but were masked by transitions of other lands to early-successional forests. A contingent valuation survey of 777 households and experimental real estate negotiations with 27 participants in Ames, IA assessed residents\u27 familiarity with alternative residential designs and estimated their willingness to pay for CSD and LID features. Most respondents were not familiar with CSD or LID features, but indicated willingness to pay for some of them (52% indicated WTP for buffered streams, 66% for rain gardens), except for clustered housing (only 27%). Negotiation participants indicated added value for homes in neighborhoods with integrated forest (+22% increment) and open spaces (+17%), and streams buffered by forest cover (+13%). A spatial hedonic model estimated effects on housing values and indicated that the presence of neighborhood-owned forest and water features had positive effects on housing prices, both increasing the value of a home by approximately 6%. Surveys and focus groups involving developers and city officials assessed their familiarity with, and interest in, alternative development designs. Developers and planners were more familiar with LID than CSD, but indicated misperceptions about both designs. Both groups also indicated a preference for alternative designs compared to standard designs. Subdivision regulations and perceived lack of demand were identified as barriers to wider implementation of CSD and LID approaches. Alternative designs could provide protection for ecosystem services in urban areas if implementation is goal-oriented, monitored for effectiveness, and when the design is configured to create broad appeal

A Consumer Perspective on Mobile Service Platforms: A Conjoint Analysis Approach

Digital platforms need to attract both application developers and end users. Existing literature suggests various strategies related to openness, flexibility, and generativity to attract application developers. However, how consumers make decisions on adopting platforms has not been studied. This paper studies which characteristics of digital platforms consumers most prefer. We focus on mobile platforms where application stores, operator portals, and service provider platforms compete for the consumer’s attention. We conducted a conjoint analysis among 166 consumers to determine the most important characteristics of the mobile platforms. We found that application-related characteristics were most important, especially the number of available applications. Governance-related and technical characteristics were hardly important. Platform characteristics were considerably less important than the brand of the operating system linked to the platform. These findings were consistent between European and Chinese users, and between males and females. The study paves the way for IS scholars to integrate consumer perspectives in the provider-dominated discourse of digital platforms

Towards a human-centric data economy

Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays. This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them. Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma [114, 115, 155]. From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services. Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, and which categories of data command higher prices. We also develop classifiers for comparing data products across different marketplaces, and we study the characteristics of the most valuable data assets and the features that specific vendors use to set the price of their data products. Based on this information and adding data products offered by other 33 data providers, we develop a regression analysis for revealing features that correlate with prices of data products. As a result, we also implement the basic building blocks of a novel data pricing tool capable of providing a hint of the market price of a new data product using as inputs just its metadata. This tool would provide more transparency on the prices of data products in the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of nascent markets. Next we turn to topics related to data marketplace design. Particularly, we study how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Regarding the selection process, we compare different purchase strategies depending on the level of information available to data buyers at the time of making decisions. A first methodological contribution of our work is proposing a data evaluation stage prior to datasets being selected and purchased by buyers in a marketplace. We show that buyers can significantly improve the performance of the purchasing process just by being provided with a measurement of the performance of their models when trained by the marketplace with individual eligible datasets. We design purchase strategies that exploit such functionality and we call the resulting algorithm Try Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N) - needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume. Our results reveal that even more complex approaches based on the “leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value from economics and game theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. However, the Shapley value entails serious computational challenges. Its exact calculation requires repetitively training and evaluating every combination of data sources and hence O(N!) or O(2N) computational time, which is unfeasible for complex models or thousands of individuals. Moreover, our work paves the way to new methods of measuring the value of spatio-temporal data. We identify heuristics such as entropy or similarity to the average that show a significant correlation with the Shapley value and therefore can be used to overcome the significant computational challenges posed by Shapley approximation algorithms in this specific context. We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some alternatives to repetitively training and evaluating a model to select data based on Try Before You Buy and approximate the Shapley value. Finally, we discuss the challenges and potential technologies that help with building a federation of standardised data marketplaces. The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue

Exploring Social Sustainability and Economic Practices

Given the three pillars of sustainability, besides the environment, the interplay of social and economic dimensions provides valuable insight into how society is molded and the key components that should be considere. In terms of social sustainability, processes and framework objectives promote the wellbeing that is integral to the balance of people, planet, and profit. Economic practices consider the system of production, resource allocation, and distribution of goods and services with respect to demand and supply between economic agents. As a result, an economic system is a variant of the social system in which it exists. At present, the forefront of social sustainability research partially encompasses the impact of economic practices on people and society, with notable emphasis centered on the urban environment. Specific interdisciplinary analyses within the scope of sustainability, social development, competitiveness, and motivational management, as well as decision making within the urban landscape, are considered. This book contains nine thoroughly refereed contributions that interconnect detailed research into the two pillars reviewed

What\u27s the Catch? An Analysis of Seafood Sustainability at the University of Vermont

Humans are fishing the oceans at a rate much faster than marine fisheries can recover, often using methods that are damaging to the marine environment. Research has revealed the complexity of issues within how seafood travels from the seas to consumers\u27 plates. Sustainable seafood certification programs have grown in popularity as public pressure demands certain practices of the seafood industry, and seafood guides have increased public awareness and highlighted the power of consumer choice. The University of Vermont (UVM) has taken steps to provide local and organic food that is sustainably harvested, but has not done the same for seafood. This research is an analysis of seafood at UVM, and it attempts to understand where the seafood served at UVM comes from. Additionally, through document research, interviews, and collaboration with UVM Dining Services, this thesis investigates UVM\u27s initiation of a sustainable seafood effort on campus and proposes recommendations as alternatives to the seafood currently offered, recommendations which may align more closely with UVM\u27s commitment to environmental sustainability and social responsibility

The Cinderella moment:Exploring consumers’ motivations to engage with renting as collaborative luxury consumption mode

Past literature argued that the purchase of luxury goods is driven by people’s motivation to conform or fit into our economic and social system. In this study, the authors focus on a new aspect of consumption, i.e. renting instead of purchasing luxury goods, backed by the emerging opportunities of sharing economy platforms. Drawing upon the analysis of spontaneous consumers’ online communications (in the form of tweets), this research aims to investigate the motivations to engage with luxury garment renting within a collaborative consumption context. To this end, a series of automatic content analyses, via two studies, were conducted using the tweets posted with respect to the Run the Runway collaborative consumption platform. Results demonstrate consumers’ increased willingness to show their social status through renting rather than owning luxurious apparel based on five main motivators (need to wear new clothes for a special event, inspirations created by the products/brands, possibility to explore a new way of consuming luxury goods, need to make more sustainable choices, and to increase the life cycle of each luxury product). The implications of these findings are discussed, while they pave the way for future research in collaborative consumption of luxury retailing