Social Media

Tracking the evolution of crisis processes and mental health on social media during the COVID-19 pandemic

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.

I Want to Break Free! Recommending Friends from Outside the Echo Chamber

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.

Forecasting mental health and emotions based on social media expressions during the COVID-19 pandemic

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.

Lo que Twitter nos cuenta de la crisis del COVID-19 y sus efectos en la salud mental en Argentina

Lo que Twitter nos cuenta de la crisis del COVID-19 y sus efectos en la salud mental en Argentina

Influence and performance of user similarity metrics in followee prediction

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.

Tracking the evolution of crisis processes and mental health on social mediaduring the COVID-19 pandemic

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.

Faking It!

This Java tool aims at facilitating the creation of datasets from social media including not only the shared content, but also the social context of the content.

Hate Speech Bias

Hate speech is in the eye of the beholder Exploring bias on hate perception

Digital Citizens

The Role of Social Media in e-Participation in Argentina

Faking It!

Faking It! A fake news multi-sourced dataset powered by Social Media