João Phillipe Cardenuto

José Nascimento, João Phillipe Cardenuto, Jing Yang et al.

Social media has become an important data source for event analysis. When collecting this type of data, most contain no useful information to a target event. Thus, it is essential to filter out those noisy data at the earliest opportunity for a human expert to perform further inspection. Most existing solutions for event filtering rely on fully supervised methods for training. However, in many real-world scenarios, having access to large number of labeled samples is not possible. To deal with a few labeled sample training problem for event filtering, we propose a graph-based few-shot learning pipeline. We also release the Brazilian Protest Dataset to test our method. To the best of our knowledge, this dataset is the first of its kind in event filtering that focuses on protests in multi-modal social media data, with most of the text in Portuguese. Our experimental results show that our proposed pipeline has comparable performance with only a few labeled samples (60) compared with a fully labeled dataset (3100). To facilitate the research community, we make our dataset and code available at https://github.com/jdnascim/7Set-AL.

João Phillipe Cardenuto, Jing Yang, Rafael Padilha et al.

Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic realities and their implications for Digital Forensics and society at large within the rapidly advancing field of AI. We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. This is especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. Additionally, we shed light on the key research challenges that lie ahead in this area. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic Science.

João Phillipe Cardenuto, Sara Mandelli, Daniel Moreira et al.

Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image).

João Phillipe Cardenuto, Ana Carolina Monari, Michelle Diniz Lopes et al.

Over the past decade, Brazil has experienced a decline in vaccination coverage, reversing decades of public health progress achieved through the National Immunization Program (PNI). Growing evidence points to the widespread circulation of vaccine-related misinformation -- particularly on social media platforms -- as a key factor driving this decline. Among these platforms, Telegram remains the only major platform permitting accessible and ethical data collection, offering insight into public channels where vaccine misinformation circulates extensively. This data paper introduces a curated dataset of about four million Telegram posts collected from 119 prominent Brazilian anti-vaccine channels between 2020 and 2025. The dataset includes message content, metadata, associated media, and classification related to vaccine posts, enabling researchers to examine how false or misleading information spreads, evolves, and influences public sentiment. By providing this resource, our aim is to support the scientific and public health community in developing evidence-based strategies to counter misinformation, promote trust in vaccination, and engage compassionately with individuals and communities affected by false narratives. The dataset and documentation are openly available for non-commercial research, under strict ethical and privacy guidelines at https://doi.org/10.25824/redu/5JIVDT