Datasets 

A lot in vera.ai centers around data and code. In other words: the dealing with data, its analysis, experimentation, and more. In October 2024 we have started to bring together and make available all code that a) was created in and b) is used by vera.ai members in one central destination on the project website. This includes direct links to respective repositories - in most cases GitHub.  

A team of vera:ai researchers from the Fraunhofer Institute for Digital Media Technology (IDMT), Germany and the Centre for Research and Technology Hellas (CERTH-ITI), Greece  have developed a new synthetic speech detection dataset. Find out more why they did it and what this contains.

This post explains the basics behind a paper entitled “VERITE: a robust benchmark for multimodal misinformation detection accounting for unimodal bias”, published in the International Journal of Multimedia Information Retrieval (IJMIR). It has been authored by researchers of the mever group at project partner CERTH-ITI.

A project by vera.ai, entitled “The Persistence and Resilience of Misinformation in the Face of Fact Checking,” was facilitated by Fabio Giglietto, Massimo Terenzi ( UNIURB) and Richard Rogers (UvA) at the Digital Media Initiative Winter School at the University of Amsterdam. We are pleased to share with you the results.

The EMNLP (Empirical Methods in Natural Language Processing) conference is one of the top NLP conferences. KInIT researchers Róbert Móro and Ján Čegiň presented three full papers at this prestigious event. Here’s more about what they did, and the related works.

A new paper that includes recent findings of disinformation analysis has been published in the Journal of Quantitative Description: Social Media. It is entitled “Cracking Open the European Newsfeed”, co-authored by vera.ai team members based at University of Urbino “Carlo Bo”, Italy. The paper contributes to the ongoing effort to describe and quantify the quality of information shared on large social media platforms.

Dataset of 9.000 AI-generated images described in the paper "Synthbuster: Towards Detection of Diffusion Model Generated Images" (Quentin Bammey, 2023, Open Journal of Signal Processing).

Here's a video recording of a talk by Fabio Giglietto of the University of Urbino, given in June 2023 at the Queensland University of Technology (QUT). Topic: using OpenAI models to identify the most salient topics circulated via Facebook links in the run-up to the Italian general elections.

In this piece we present findings from a study on current fact-checking archiving practices and Facebook post removals using the “War in Ukraine” dataset of the European Digital Media Observatory (EDMO). Insights presented here come from a project that was carried out as part of the Digital Methods Initiative Winter School and Data Sprint 2023.

Here's another project that was carried out during the 2023 Winter School organised by the University of Amsterdam’s Digital Methods Initiative (DMI). The work of this team, coordinated by the hosts themselves, among other tested a "detection method” that is to allow fact-checkers for faster detection of “post-truth spaces” and verification of actors that spread (in this case pro-Kremlin) propaganda on social networks.