The ongoing discourse in social media has amplified the fears, uncertainties, and doubts (FUD) surrounding COVID-19 and the currently available vaccines, leading to an infodemic (a portmanteau of information and epidemic, referring to the spread of potentially accurate and inaccurate information about a disease spreading like an epidemic).
More and more anti-government protests have occurred in the world in recent years. A common denominator for such protests is that they all rely on social media.
In present times, Online Social Media (OSM) platforms such as Facebook, YouTube, and Twitter, have been used by billions of individuals for establishing a narrative, conducting propaganda, and disseminating misinformation.
Fake News on social media platforms has received a lot of attention in recent years, notably for incidents relating to politics (the 2016 US Presidential election) and healthcare (the COVID-19 infodemic, to name a few). Several approaches for identifying fake news have been presented in the literature. The methodologies range from network analysis techniques to Natural Language Processing (NLP) and the use of Graph Neural Networks (GNNs).
The recommender systems employed by digital media platforms typically showcase content that is similar to the content a user has already consumed. When that content is biased, this runs the risk of fuelling radicalization. One option to mitigate the radicalization risks posed by digital media platforms is to modify their recommendations.
On November 18th, a special session dedicated to the SoBigData Research Infrastructure was held as part of the second edition of the International Forum on Digital and Democracy, a two-day conference which has the purpose of convening academics, researchers and policymakers to discuss the relationship between digital technologies and democratic institutions and to debate and exchange information, ideas and good practices about Digitalization and Democracy.
The new version of scikit-mobility includes a “data module” to access and download ready-to-use mobility datasets and upload new ones to the collection!
As data scientists, we all know the convenience of having ready-to-use datasets at your fingertips. When performing exploratory data analysis (EDA), testing hypotheses, and prototyping models, nothing can beat having preprocessed and reliable datasets for experimentation.
An accurate estimate of athletes’ body composition may vary in accordance with the different assessment techniques used.