The spread of climate change mis- and disinformation poses a big threat to society; addressing this is the goal of the Joint Interdisciplinary Project (JIP) 6.1.1, in collaboration with the National Police. This report details the development of the Climate Disinformation Tracker, an open-source proof-ofconcept tool designed to trace the earliest online occurrence of climate denial narratives on Platform X and provide insightful visualizations of related tweets. The methodology, adapted from the DisTrack architecture, utilizes KeyBERT for keyword extraction and a custom scraping pipeline relying on the Nitter front-end for data retrieval, followed by mDeBERTa-v3-base-mnli-xnli for natural language inference (NLI) to classify posts as entailing, neutral, or contradictory to a user-provided claim. Validation testing demonstrated that the tool correctly identified the source tweet in 72% of claims when incorporating the synonym component, thus validating the potential of this approach for misinformation
tracking. The primary constraints identified are the dependence on non-deterministic Nitter scraping, which introduces operational instability and a 500-character query limit, and the accuracy ceiling of the alignment model. Despite these limitations, the tool validates a functional approach for empowering the public and investigative journalists with traceable context.

Smoking remains one of the largest health concerns worldwide, which is why eHealth applications with virtual coaches have been developed to assist smokers with quitting. Providing additional feedback from human coaches during such smoking cessation programs can further improve the effectiveness of the intervention. However, due to budgetary constraints and the limited availability of human coaches, it is important to make informed decisions about when someone gets human support to optimize the effectiveness. This research investigates the use of reinforcement learning (RL) to determine when to provide human feedback in quitting smoking with a virtual coach. Using data from a longitudinal study, we implemented an RL model that decides when to involve a human coach based on users' appreciation for human support and their self-efficacy, optimizing the effort that people spend on preparatory activities and their likelihood of returning to the program. Results show that the model is effective in allocating human support, increasing users' effort and return likelihood while considering the cost of human coaches. These findings support using RL to help with determining when to provide human support in smoking cessation programs.