Human flourishing is a thriving concept, whose use has greatly increased among academic researchers from a variety of fields, from the arts and humanities and psychology to the social and environmental sciences and economics. To better understand the concept’s success, this work proposes a bibliometric review, in which statistical methods and data mining were used to analyze 1829 documents, chosen from the Scopus database by searching the term “human flourishing”. Through cluster and network analyses, the study shows the concept’s evolution and composition, as well as its current tensions and trends, in which the predominantly psychological approach is being compensated with social concerns and the search for justice. Furthermore, the concept’s strong philosophical roots provide it with abstract richness and great fertility, which can be seen in keywords, such as virtue or eudaimonia. This bibliometric review proved to be useful for this type of study, despite the limitations imposed by the characteristics of the Scopus database itself.

Cuando comenzó la pandemia de COVID 19, las plataformas sociales tuvieron un rol central en la producción y acceso a la información. Este estudio identifica los tópicos de mayor interés y sus sentimientos asociados en Twitter en la conversación pública en lengua española en ese periodo. Asimismo, analiza el rol de Twitter como plataforma social involucrada en la conversación pública, como medio para la autocomunicación de masas y para amplificar la voz de un conjunto reducido de actores de alta visibilidad. Se recolectaron 231.375 tuits en España y América Latina durante dos meses. Se midieron indicadores de frecuencia y sentimientos, y se agruparon términos para identificar tópicos y determinar el interés de los usuarios sobre estos, mediante métodos digitales y de lenguaje computacional en R. La frecuencia de los principales términos es dinámica a lo largo del período estudiado, lo que sugiere diferentes percepciones de la pandemia. Los tópicos principales refieren a conversaciones en torno a la cantidad de casos, muertos y contagiados, con prevalencia de sentimientos negativos. La muestra analizada corresponde a mensajes generados por usuarios comunes en su gran mayoría, pero una parte de ella ha sido amplificada a gran escala mediante retuits y marcas de favoritos.

The power of social media in spreading the idea of wellbeing has already been addressed by several psychologists and scholars through the analysis of the vocabulary; however, the use of the human flourishing (HF) concept in such platforms has not yet been analyzed. This study addresses such a topic by analyzing more than 600 thousand Twitter messages posted by a community of users who associate themselves with HF and comparing them to more than 400 thousand messages in other Twitter lists. The study aims to identify the HF users’ interests, the richness in their vocabulary, the feelings and emotions that they share, and the grammar used in their constructions. Such an analysis was conducted through text mining computational methods, including sentiment analysis, natural language processing (NLP), and topic modeling. The results revealed that although HF users employ average vocabulary diversity, they share more positive emotions, and a greater variety of emojis. They also tended to discuss different topics, from more spiritual and health-related subjects to more practical matters related to work and success. Finally, they generally wrote from an empathetic state of mind, caring about people’s day-to-day feelings and about the world.

This work addresses the communicative role of authorized voices on Twitter during the COVID-19 pandemic and their interaction with ordinary users. They are defined as public profile users who have a big number of followers, and whose messages are massively disseminated on the platform by ordinary users. A set of tweets was collected over two months through the Twitter API, and then a subset of data was formed with the tweets replicated more than 100 times. Labeling, data mining, and sentiment analysis techniques were applied to it. It is observed that the interpretive framework of the pandemic is modeled by the media, although there are perceptions of ordinary users about the pandemic as a time of economic, health, political and personal crisis that are not present in the authorized voices. It is concluded that the media and front-line government officials are the ones that achieved the greatest adherence and amplification of the word by ordinary users, although there is a significant gender gap between the voices of men and those of women. Este trabajo intenta comprender el funcionamiento de las voces autorizadas en Twitter durante la pandemia de COVID-19 y su interacción con los usuarios ordinarios. Se define a las voces autorizadas como usuarios de perfil público que poseen gran cantidad de seguidores, y cuyos mensajes son diseminados masivamente en la plataforma por los usuarios ordinarios. Para ello se recolectó un conjunto de tuits a lo largo de dos meses a través de la API de Twitter, y luego se formó un subconjunto de datos con los tuits replicados más de 100 veces. A este subconjunto se le aplicaron técnicas de etiquetado, minería de datos y análisis de sentimientos. Se observa que el marco interpretativo de la pandemia se encuentra modelado por los medios de comunicación, aunque existen percepciones propias de los usuarios ordinarios acerca de la pandemia como un momento de crisis económica, de salud, política y personal que no se encuentran presentes en las voces autorizadas. Se concluye que los medios de comunicación y los funcionarios de gobierno de primeras líneas son los que lograron mayor adhesión y amplificación de la palabra por parte de los usuarios ordinarios, aunque se observa una brecha de género importante entre las voces de los hombres y las de las mujeres.

The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities.