The field of online risk assessment and disinformation research is rapidly evolving, with a growing focus on developing innovative methods to detect and mitigate the spread of false information. Recent studies have explored the use of machine learning techniques, such as meta-analysis and deep learning models, to improve the accuracy of disinformation detection. Additionally, researchers are investigating the role of social media bots in spreading disinformation and the potential of digital nudges to reduce online disinformation sharing. The development of open-source platforms, such as DISINFOX, is also facilitating the collection, management, and dissemination of disinformation incidents and influence operations. Noteworthy papers include: Digital Nudges Using Emotion Regulation to Reduce Online Disinformation Sharing, which found that distraction nudges can effectively reduce the sharing of disinformation driven by strong anger. DISINFOX, an open-source threat intelligence exchange platform that provides a unified view of cybersecurity and disinformation events. Is Less Really More? Fake News Detection with Limited Information, which proposed a framework for detecting fake news using systematically selected, limited information. Disinformation about autism in Latin America and the Caribbean, which investigated the structuring, articulation, and promotion of autism-related disinformation in conspiracy theory communities on Telegram.