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.

Automatic Speech Recognition (ASR) systems are becoming increasingly popular in this day and age. Unfortunately, due to inherent biases within these systems, performance disparities exist among specific demographic groups. Bias metrics can be used to measure this bias. Within ASR they represent a niche area that has not yet been thoroughly explored. The few bias metrics that exist in literature mainly centre around the performance differences between speaker groups. This paper proposes two new bias metrics that focus not only on performance differences, but also take the base performance into account: Weighted Performance Bias (WPB) and Intergroup Weighted Performance Bias (IWPB). Although the lack of ground truth makes the results less easily interpretable, the results show similar trends within the new metrics as those defined in literature: bias is greatest among non-native Dutch speech.