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

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

Strategic sentiments and emotions in post-Second World War party manifestos in Finland

We contribute to the growing number of studies on emotions and politics by investigating how political parties strategically use sentiments and emotions in party manifestos. We use computational methods in examining changes of sentiments and emotions in Finnish party manifestos from 1945 to 2019. We use sentiment and emotion lexicons first translated from English into Finnish and then modified for the purposes of our study. We analyze how the use of emotions and sentiments differs between government and opposition parties depending on their left/right ideology and the specific type of party manifesto. In addition to traditional sentiment and emotion analysis, we use emotion intensity analysis. Our results indicate that in Finland, government and opposition parties do not differ substantially from each other in their use of emotional language. From a historical perspective, the individual emotions used in party manifestos have persisted, but changes have taken place in the intensity of using emotion words. We also find that in comparison with other parties, populist parties both appeal to different emotions and appeal to the same emotions with different intensities.Peer reviewe

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

The IDEA of Us : An Identity-Aware Architecture for Autonomous Systems

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by $13.6\%$

The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6

Social Informatics : 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part II

Given the dominance of online platforms in attracting consumers and advertisers, online publishers are squeezed between declining traffic and advertising revenues from their website content. In turn, super platforms, the dominant content dissemination platforms, such as Google and Facebook, are monetizing online content at the expense of publishers by selling ad impressions in advertising auctions. In this work, we analyze publishers’ possibilities of forming a coalition and show that, under a set of assumptions, the optimal strategy for publishers is cooperation against a super platform rather than posting content on the super platform. Not choosing to publish on a super platform can yield the whole coalition more traffic, enabling some individual publishers to recoup the lost traffic. We further show that if the coalition does not forbid diversification, most publishers choose both coalition and super platform.</p