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The purpose of this paper is to present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on social media contents.

Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, the authors build time series models that describe the evolution of markers and their correlation with crisis events. Second, the authors use the time series for forecasting markers and identifying high prevalence points for the estimated markers.

The authors evaluated different forecasting strategies that yielded different performance and capabilities. In the best scenario, high prevalence periods of emotions and mental health issues can be satisfactorily predicted with a neural network strategy, even at early stages of a crisis (e.g. a training period of seven days).

This work contributes to a better understanding of how psychological processes related to crises manifest in social media, and this is a valuable asset for the design, implementation and monitoring of health prevention and communication policies.

Although there have been previous efforts to predict mental states of individuals, the analysis of mental health at the collective level has received scarce attention. The authors take a step forward by proposing a forecasting approach for analyzing the mental health of a given population at a larger scale.

Followee recommendation is a problem rapidly gaining importance in Twitter as well as in other micro-blogging communities. To find interesting users to follow, most recommendation systems leverage different factors such as graph topology or user-generated content, among others. Those systems mostly disregard, however, the effect of psychological characteristics, such as personality, over the followee selection process. As personality is considered one of the primary factors that influence human behaviour, the purpose of this paper is to shed some light on the impact of personality traits on followee selection.

The authors performed a data analysis comparing the similarity among Twitter users and their followees regarding personality traits. The authors analysed three different similarity measures. First, the authors computed an overall similarity considering the five personality traits or dimensions of the Five-Factor model as a whole. Second, the authors computed the dimension-to-dimension similarity considering each individual personality trait independently of each other. Third, the authors computed a cross-dimension similarity considering each personality dimension in relation to the others.

This study showed that personality should be considered as a distinctive factor in the process of followee selection. However, personality dimensions should not be analysed as a whole as the overall personality similarity might not accurately assess the actual matching between individuals. Instead, the performed data analysis showed the existence of relations among the individual dimensions. Thus, the importance of considering each personality trait with respect to others is stated.

This study is among the firsts to study the impact of personality, one of the primary factors that influence human behaviour and social relationships, in the selection of followees in micro-blogging communities.