Integrating Cultural Perspectives in the iField: The Case of Asian Informatics

This research study justifies Asian informatics as an emerging area of research in the information field (iField) and demonstrates its potential to facilitate diversity of library and information science (LIS) education in the U.S. by offering a cross-cultural perspective in this increasingly multicultural information age. Providing a critical analysis of the iField doctoral education in the U.S., this paper demonstrates the needs and benefits of integrating Asian informatics as part of the LIS curricula, urging LIS education to raise cultural awareness in information studies

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics

The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.This work would not have been accomplished without the financial support of CONICYT-PFCHA/DOCTORADO BECAS CHILE/2019-21190345. The last author received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the author participates in Elkartek projects KK-2022/00051 and KK-2021/00070. The Spanish MCIN has also granted the author a research project under code PID2020-116346GB-I00

Semantic Analysis of Vaccine and Mask Sentiments in COVID-19 Twitter Data

SARS CoV-2 (COVID-19) was identified as the cause of severe respiratory disease in China in 2019. It is a virus that will be transferred person-to-person by sneezing, coughing, or talking. This phenomenon not only affects public health and economics but also mental health as well. SARS-CoV-2 vaccines and wearing masks plays significant rolesin preventing the spread of the COVID-19 virus, but vaccine hesitancy and anti-mask beliefs threaten the efficacy of the government orders in prevention and immunization against Coronavirus. The impact of the COVID-19 pandemic has been investigated from different aspects, but few large-scale studies focus on the opinion of people toward government orders to wear face mask and get vaccination. The abundant data on online social media however enables researchers to analyze people\u27s attitudes toward vaccination and the use of face mask. In this study, we use Twitter API and scrape 340 million COVID-19 tweets posted in the timeline of December 2020 to March 2021. Our goal is to investigate how people respond to tweets about masking and vaccines as a means of understanding sentiments towards both practices. Specifically, we focus on which tweets about the topics tend to become viral relative to those that are neither retweeted nor receive any replies. Toward this end, we split the dataset into three categories: 1) replied tweets 2) retweeted tweets, and 3) no-engagement tweets which are tweets that receive no response. We then deploy topic modeling to identify the most popular tweet topics in each category. Furthermore, we filter tweets for vaccine and mask related hashtags and use the algorithm,VADER to find the sentiment of these tweets. By applying topic modeling and Vader, we assess the vaccine and mask-related sentiment scores and visualize their progression during four months. Our analysis indicates a slight difference in the distribution of tweets with positive and negative sentiments with vaccination or mask hashtags, with the dominant polarity of positive sentiments. Despite the overall strength of positive stances, negative opinions about COVID-19 vaccines and masks remain among people who are hesitant towards wearing face masks and vaccination. We also investigate and show that sentiments among Twitter users shift from positive to negative and vice versa over time. The most probable reasons for the domination of positive sentiments in tweets with vaccine and mask hashtags, appears to be the belief that such tweets are providing accurate information and also because of the risks of COVID-19 as discussed by well-regarded organizations. At the same time, however, inaccurate information, mistrust of well-regarded organizations or media, and the influence of celebrities on their followers does push a segment of users into hesitancy and negative views about masks and vaccination

Online social integration of migrants: Evidence from Twitter

As online social activities have become increasingly important for people’s lives, understanding how migrants integrate into online spaces is crucial for providing a more complete picture of integration processes. We curate a high-quality data set to quantify patterns of new online social connections among immigrants in the United States. Specifically, we focus on Twitter and leverage the unique features of these data, in combination with a propensity score matching technique, to isolate the effects of migration events on social network formation. The results indicate that migration events led to an expansion of migrants’ networks of friends on Twitter in the destination country, relative to those of similar users who did not move. Male migrants between 19 and 29 years old who actively posted more tweets in English after migration also tended to have more local friends after migration compared to other demographic groups, indicating the impact of demographic characteristics and language skills on integration. The percentage of migrants’ friends from their country of origin decreased in the first few years after migration and increased again in later years. Finally, unlike for migrants’ friends’ networks, which were under their control, we did not find any evidence that migration events expanded migrants’ networks of followers in the destination country. While following users on Twitter in theory is not a geographically constrained process, our work shows that offline (re)location plays a significant role in the formation of online networks

Las redes sociales como instrumento de gestión de destinos turísticos

The main objective of this research focuses on determining the functions and application of social networks in the management of tourist destinations, with the aim of revealing the state of the art and degree of applicability. First, in order to fulfill the objectives and theoretical hypotheses, a bibliographic analysis is carried out, which leads to the elaboration of the state of the art regarding the topic on which this doctoral thesis revolves. In this sense, the state of the art of the research is elaborated from a systematic analysis of the scientific literature on smart destinations (concept, dimensions, components, management systems) and their integration with social networks. In order to respond to the second group of specific objectives and hypotheses, the methodology applied was quantitative, based on the analysis of a series of data from Spanish tourist destinations in terms of their presence and management of social networks. The quantification of these variables, for each of the 78 tourist destinations (among which were all the smart tourist destinations, hosted by the SEGITTUR project), allowed us to apply different quantitative statistical techniques, such as: a) Pearson's correlation analysis, to establish the type of interrelation between the independent variable (number of visitors) and the dependent variables, which referred to the presence and management of the destinations on the web and in social networks; and b) to determine the degree of use of social networks by the smart destinations with respect to the others, an ANOVA analysis was carried out between the variables of the most visited destinations with respect to those of the smart destinations, in order to detect possible statistically significant differences between the two groups of destinations with respect to their management of social networks. Finally, in order to fulfill the third group of objectives and specific hypotheses, and to demonstrate whether there is complementarity between the data provided by social networks and those offered by official statistics, in terms of tourism demand, a qualitative methodology is followed, since it is based on an exploratory case analysis. In this sense, the change experienced by the behaviors and feelings of tourists visiting Andalusia as a result of COVID-19 is analyzed, both with data from the Andalusia Tourism Situation Survey (ECTA, 2020) and by means of a sentiment analysis using Twitter data. For the exploratory sentiment analysis, using Twitter, the statistical program R and the library package (rtweet) were used to retrieve messages from the social network Twitter (tweets). Machine learning sentiment analysis algorithms were then applied to the resulting data. Therefore, based on the results obtained from this doctoral thesis, we believe that it is necessary for tourist destinations to have a professional specialized in the management of social networks (social media manager), as this will allow the destination to make the most of its presence in social networks. In short, it is considered that research focused on the applicability of social networks to the management processes of tourist destinations is still in its early stages of development, especially if we analyze the real applicability it is having in specific tourist destinations. This recommendation is important both when it comes to adapting to the progressive development of new technologies, as well as to the evolution of the behavior and profile of tourists, who are increasingly familiar with the use of new technologies, and demand flexible experiences adapted to their preferences, among other characteristics.El objetivo principal de esta investigación se centra en determinar las funciones y aplicación de las redes sociales en la gestión de los destinos turísticos, con objeto de poner de manifiesto el estado de la cuestión y grado de aplicabilidad. Primero para dar cumplimiento a los objetivos e hipótesis teóricas, se realiza un análisis bibliográfico, el cual da lugar a la elaboración del estado del arte respecto al tema sobre el que gira la presente tesis doctoral. En este sentido, el estado del arte de la investigación se elabora a partir de un análisis sistemático de la literatura científica acerca de los destinos turísticos inteligentes (concepto, dimensiones, componentes, sistemas de gestión) y su integración con las redes sociales. Para dar respuesta al segundo grupo de objetivos e hipótesis específicas, la metodología aplicada fue de tipo cuantitativa, basada en el análisis de una serie de datos que arrojan los destinos turísticos españoles en cuanto a su presencia y gestión de las redes sociales. La cuantificación de estas variables, para cada uno de los 78 destinos turísticos (entre los que se encontraban todos los destinos turísticos inteligentes, acogidos al proyecto de SEGITTUR), nos permitió aplicar diferentes técnicas estadísticas cuantitativas, tales como: a) el análisis de correlación de Pearson, para establecer el tipo de interrelación entre la variable independiente (número de visitantes) y las variables dependientes, que se referían a la presencia y gestión de los destinos la web y en las redes sociales; y b) para determinar el grado de utilización de las redes sociales por parte de los destinos turísticos inteligentes respecto a los restantes, se realizó un análisis ANOVA entre las variables de los destinos más visitados respecto a las de los destinos turísticos inteligentes, con objeto de detectar posibles diferencias estadísticamente significativas entre ambos grupos de destinos en lo que respecta a la gestión que hacen de las redes sociales. Por último, para dar cumplimiento al tercer grupo de objetivos e hipótesis específicas, y demostrar si existe complementariedad entre los datos que arrojan las redes sociales y los que ofrecen las estadísticas oficiales, en lo que respecta a la demanda turística, se sigue una metodología de corte cualitativa, ya que se fundamenta en un análisis de caso, de carácter exploratorio. En este sentido, se analiza el cambio experimentado por los comportamientos y sentimientos de los turistas que visitan Andalucía como consecuencia de la COVID-19, tanto con los datos de la Encuesta de Coyuntura Turística de Andalucía (ECTA, 2020) como mediante un análisis de sentimientos con datos de Twitter. Para el análisis exploratorio de sentimientos, mediante Twitter, se utilizó el programa estadístico R y el paquete de biblioteca (rtweet) para la recuperación de mensajes de la red social Twitter (tweets). A continuación, se aplicaron algoritmos de análisis de sentimientos mediante aprendizaje automático a los datos resultantes. Por todo ello, a partir de los resultados que se obtienen de esta tesis doctoral, consideramos que se hace necesario que los destinos turísticos cuenten con un profesional, especializado en la gestión de redes sociales (social media manager), pues ello permitirá al destino sacar el máximo provecho a su presencia en las redes sociales. No en vano, esta actuación posibilitará el máximo desempeño de las múltiples funciones que, a lo largo de la investigación, se han puesto de manifiesto, que pueden desempeñar esta herramienta, dentro de los procesos de gestión de los destinos turísticos En definitiva, se considera que la investigación centrada en la aplicabilidad de las redes sociales a los procesos de gestión de los destinos turísticos está aún en sus primeras etapas de desarrollo, sobre todo si analizamos la aplicabilidad real que está teniendo en destinos turísticos concretos. Esta recomendación es importante tanto a la hora de adaptarse al progresivo desarrollo de las nuevas tecnologías, como por la evolución que viene experimentando el comportamiento y perfil de los turistas, los cuales, cada vez, están más familiarizados con el uso de nuevas tecnologías, y demandan experiencias flexibles y adaptadas a sus preferencias, entre otras características

Medical Brain Drain From Southeastern Europe: Using Digital Demography to Forecast Health Worker Emigration

Background: This paper shows that the tools of digital demography, such as Google Trends, can be used for determining, estimating, and predicting the migration of health care workers (HWs), in this case, from Croatia and the Western Balkans (WB) to Germany and Austria. Objective: This study aims to test the usefulness of Google Trends indexes to forecast HW migration from Croatia and the WB to Germany and Austria. The paper analyzes recent trends in HW mobility in Europe and focuses specifically on mobility patterns among medical doctors and nurses using digital demography. Without increased emigration in the last 10 years, Croatia and the WB would have 50% more HWs today, and this staff is now crucial in the fight against a pandemic. Furthermore, the COVID-19 pandemic contributed to the increase in emigration. Methods: A particular problem in analyzing the emigration of HCWs from Croatia and the WB is that there is no system for monitoring this process. Official data is up to 3 years late and exists only for persons deregistered from the state system. Furthermore, during the pandemic, the "normal" ways of data collection are simply too slow. The primary methodological concept of our approach is to monitor the digital trace of language searches with the Google Trends analytical tool. To standardize the data, we requested the data from January 2010 to December 2020 and divided the keyword frequency for each migration-related query. We compared this search frequency index with official statistics to prove the significance of the results and correlations, and test the model's predictive potential. Results: All tested migration-related search queries, which indicate HCWs' emigration planning, showed a positive linear association between Google index and data from official statistics (Organisation for Economic Co-operation and Development: Serbia R2=0.3381, Bosnia and Herzegovina [B&H] R2=0.2722, Croatia R2=0.4515). Migration-related search activities such as "job application + nurses" from Croatia correlate strongly with official German data for emigrated HWs from Croatia, Serbia, and B&H. Decreases in Google searches were correlated with the decrease in the emigration of HWs. Thus, this method allows reliable forecasts for the future. Conclusions: This paper highlights that the World Health Organization’s list of countries with HWs shortages should be updated to include Croatia and the countries from the WB. The issue of the European Union drawing HWs from the EU periphery (Croatia) and nearby countries (B&H, Serbia) clearly shows a clash between the EU freedom of movement and the right to health care and a need to ensure a health care workforce in all European regions. Understanding why HWs emigrate from Croatia and the WB, and the consequences of this process are crucial to enabling state agencies and governments to develop optimal intervention strategies to retain medical staff. The benefit of this method is reliable estimates that can enable a better response to a possible shortage of HWs and protect the functioning of the health system. The freedom of movement of workers in the EU must be supplemented with a common pension and health care system in the EU

An ambient agent model for reading companion robot

Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida