Resources

The Hatedemics Platform is an interactive web platform developed within the European HATEDEMICS project to support the analysis, understanding, and prevention of online hate speech, disinformation, and harmful information ecosystems.

Based on artificial intelligence, network science, and computational social science techniques, the platform provides interactive visualisations of Telegram networks, educational resources, and counterspeech tools that help users explore the structure and dynamics of online communities.

The platform also provides educational toolkits, open-source software, and machine learning models for hate speech and checkworthiness detection, supporting researchers, educators, journalists, NGOs, and policymakers in promoting safer digital spaces.

The source code of the platform, educational toolkits (EN, IT, ES, PL and MT), dialogue datasets, hate speech annotation datasets and machine learning models for hate speech and checkworthiness detection are openly available. 

The Telegram datasets powering the platform are available upon request for research purposes. Requests for data access should be directed to Lucila G. Alvarez-Zuzek (Fondazione Bruno Kessler).

The Infodemic Observatory for COVID-19 is a work advised by the World Health Organization (WHO) to better understand the impact of an infodemic at global scale. 

Based on artificial intelligence techniques, data science and network science, the Observatory provides in almost real time a unique lens on the current status of misinformation and disinformation diffusing in the popular microblogging platform Twitter.

The computational tool, publicly accessible, is the result of an interdisciplinary collaboration involving physicists, computer scientists, cultural economists, public health and communication experts from FBK and advised by WHO.

A video abstract created by Research Square for the manuscript titled "Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities", selected as the winner of the Article of the Year Award 2021 by the journal Complexity. 

A large scale public-domain dataset (CC0 1.0) available on Dryad Data, where we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole British public transport system for a week in October 2010. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods.