We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks.
Category: social science
Online courses on Data, Policies, and COVID-19
Five online courses (MOOCs) that collect technical and policy solutions to pandemic challenges have been published on Coursera
The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
Despite the increasing limitations for unvaccinated people, in many European countries, there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. Within the PERISCOPE project, we studied the role of online social media in influencing individuals' opinions about getting vaccinated by designing a … Continue reading The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
The Final TRIGGER Conference
We will join and contribute to the final TRIGGER conference is scheduled for May 31st, 2022 in Brussels. The theme is: "Rethinking the EU’s role in global governance". In this context, the TRIGGER project is going to present the main research outcomes of the H2020 research program that started in 2018, setting the stage for the collaboration … Continue reading The Final TRIGGER Conference