This study investigates the extent to which linguistic typology influences the performance of two automatic speech recognition (ASR) systems across diverse language families. Using the FLEURS corpus and typological features from the World Atlas of Language Structures (WALS), we analysed 40 languages grouped by phonological, morphological, syntactic, and semantic domains. We evaluated two state-of-the-art multilingual ASR systems, Whisper and Seamless, to examine how their performance, measured by word error rate (WER), correlates with linguistic structures. Random Forests and Mixed Effects Models were used to quantify feature impact and statistical significance. Results reveal that while both systems leverage typological patterns, they differ in their sensitivity to specific domains. Our findings highlight how structural and functional linguistic features shape ASR performance, offering insights into model generalisability and typology-aware system development.

Hassles and uplifts are psychological constructs of individuals’ positive or negative responses to daily minor incidents, with cumulative impacts on mental health. These concepts are largely overlooked in NLP, where existing tasks and models focus on identifying general sentiment expressed in text. These, however, cannot satisfy targeted information needs in psychological inquiry. To address this, we introduce Hassles and Uplifts Detection (HUD), a novel NLP application to identify these constructs in social media language.We evaluate various language models and task adaptation approaches on a probing dataset collected from a private, real-time emotional venting platform. Some of our models achieve F scores close to 80%. We also identify open opportunities to improve affective language understanding in support of studies in psychology.

Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.