Although clients’ perceptions of therapist empathy are known to correlate with therapy effectiveness, the specific ways that the therapist’s language use contributes to perceived empathy remain less understood. Natural Language Processing techniques, such as transformer models, permit the quantitative, automated, and scalable analysis of therapists’ verbal behaviors. Here, we present a novel approach to extract linguistic features from text-based crisis intervention transcripts to analyze associations between specific crisis counselor verbal behaviors and perceived genuine concern. Linguistic features associated with higher perceived genuine concern included positive emotional language and affirmations; features associated with lower perceived genuine concern included self-oriented talk and overuse of templates. These findings provide preliminary evidence toward pathways for automating real-time feedback to crisis counselors about clients’ perception of the therapeutic relationship.

Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals. However, data about violent incidents with direct impact and relevance for humanitarian aid operations is not readily available. An automatic data collection and NLP-backed classification framework aligned with humanitarian perspectives can help bridge this gap. In this paper, we present HumVI – a dataset comprising news articles in three languages (English, French, Arabic) containing instances of different types of violent incidents categorized by the humanitarian sector they impact, e.g., aid security, education, food security, health, and protection. Reliable labels were obtained for the dataset by partnering with a data-backed humanitarian organization, Insecurity Insight. We provide multiple benchmarks for the dataset, employing various deep learning architectures and techniques, including data augmentation and mask loss, to address different task-related challenges, e.g., domain expansion. The dataset is publicly available at https://github.com/dataminr-ai/humvi-dataset.