We present a thematic study on the differences in language used in social media posts and look at indicators that reveal the distinctive stages of a crisis and the country response thereof. The analysis was combined with a study of the temporal prevalence of mental health related conversations and emotions. This approach can provide insights for public health policy design to monitor and eventually intervene during the different stages of a crisis, thus improving the adverse mental health effects on the population.
We devise FRediECH (a Friend RecommenDer for breakIng Echo CHambers), an echo chamber-aware friend recommendation approach that learns users and echo chamber representations from the shared content and past users’ and communities’ interactions.
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. 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.
The study shows how the choice of the different factors and assessment alternatives affects followee recommendation. It also verifies the existence of certain patterns regarding friends and random users' similarities, which can condition the adequacy of the available similarity metrics.
We present a thematic analysis on the differences in language used in social media posts, and look at indicators that reveal the different stages of a crisis and the country response thereof. The analysis was combined with a study of the temporal prevalence of mental health conversations across the time span.