Sympathy as a Lens for Human–Robot Interaction: Analysing YouTube Responses to Robot Abuse

When witnessing the abuse of others, humans generally exhibit emotional responses. A large number of studies in Human-Robot-Interaction (HRI) have shown that humans also react with sympathy when robots are abused, but most of these insights come from controlled laboratory studies using short videos and student samples. To complement existing research with observations drawn from real-world online discussions, this paper presents a sentiment analysis of 103,413 YouTube comments on videos depicting abuse of animal-like, humanoid, and cart-shaped robots. To validate our sentiment classification, we analysed the comments using a lexicon-based tool, two fine-tuned language models, and three general-purpose state-of-the-art large language models (LLMs). The comparison yielded interesting results: LLMs generally classified science-fiction–related comments, e.g., references to dystopian TV shows, as negative, while lexicon and fine-tuned models mainly labelled them as neutral. The six models agreed on the classification of a total of 27,427 comments, which we used to explore the sentiment expressions occurring across videos featuring robots with different physical forms. Our findings provide large-scale, ecologically valid insights into how emotional responses to robot abuse are expressed and analysed in online video platforms.