Personality trait analysis during the COVID-19 pandemic: a comparative study on social media

The COVID-19 pandemic, a global contagion of coronavirus infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has triggered severe social and economic disruption around the world and provoked changes in people’s behavior. Given the extreme societal impact of COVID-19, it becomes crucial to understand the emotional response of the people and the impact of COVID-19 on personality traits and psychological dimensions. In this study, we contribute to this goal by thoroughly analyzing the evolution of personality and psychological aspects in a large-scale collection of tweets extracted during the COVID-19 pandemic. The objectives of this research are: i) to provide evidence that helps to understand the estimated impact of the pandemic on people’s temperament, ii) to find associations and trends between specific events (e.g., stages of harsh confinement) and people’s reactions, and iii) to study the evolution of multiple personality aspects, such as the degree of introversion or the level of neuroticism. We also examine the development of emotions, as a natural complement to the automatic analysis of the personality dimensions. To achieve our goals, we have created two large collections of tweets (geotagged in the United States and Spain, respectively), collected during the pandemic. Our work reveals interesting trends in personality dimensions, emotions, and events. For example, during the pandemic period, we found increasing traces of introversion and neuroticism. Another interesting insight from our study is that the most frequent signs of personality disorders are those related to depression, schizophrenia, and narcissism. We also found some peaks of negative/positive emotions related to specific eventsOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors thank the support obtained from: i) project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next GenerationEU), ii) project PID2022-137061OB-C22 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; suppported by the European Regional Development Fund) and iii) Consellería de Educación, Universidade e Formación Profesional (accreditation 2019-2022 ED431G-2019/04, ED431C 2022/19) and the European Regional Development Fund, which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University SystemS

Fusión de información para detección de trastornos mentales: BERT multimodal contra múltiples BERTs fusionados

Given the increasing number of modalities that modern classification problems provide, recently a multimodal BERT transformer (MMBT) was proposed. An interesting opportunity to evaluate the effectiveness of such model is posed by the problem of timely detection of mental disorders of social media users. For this problem, a multi-channel perspective involves extracting from each user post different types of information, such as thematic, emotional and stylistic content. This study evaluates the suitability of tackling this problem by the apparently ad-hoc MMBT, moreover, we further evaluate if regular BERT models could be combined or fused in such a way that could have a chance in a multi-channel arena. For the evaluation, we use recent public data sets for three important mental disorders: Depression, Anorexia, and Self-harm. Results suggest that BERT models can get on their own a data representation that could be later fusioned and boost the classification performance by at least 5% in F1 measure, even surpassing the MMBT.Dado el creciente número de modalidades que ofrecen los problemas de clasificación modernos, recientemente se ha propuesto un transformer BERT multimodal (MMBT). Una oportunidad interesante para evaluar la eficacia de dicho modelo la plantea el problema de la detección oportuna de los trastornos mentales de usuarios de las redes sociales. Para este problema, una perspectiva multicanal implica extraer de cada post de los usuarios diferentes tipos de información, como su contenido temático, emocional y estilístico. Este estudio evalúa la idoneidad de abordar este problema mediante el aparentemente ad-hoc MMBT, además, evaluamos si los modelos BERT regulares podrían combinarse o fusionarse de tal manera que pudieran tener una oportunidad en un escenario multicanal. Para la evaluación, utilizamos conjuntos de datos públicos recientes para tres importantes trastornos mentales: Depresión, Anorexia y Autolesiones. Los resultados sugieren que los modelos BERT pueden obtener por sí solos una representación de los datos que podría fusionarse posteriormente y aumentar el rendimiento de la clasificación en al menos un 5% en la medida F1, superando incluso al MMBT