Public awareness of Autism Spectrum Disorder (ASD) has grown in recent years, yet stigma surrounding this condition persists. Building on prior research showing increasingly positive portrayals of ASD, this study examines recent longitudinal trends in stigma and ASD, with a focus on Italian newspapers, and how these were affected by a key event such as the COVID-19 pandemic. We analyzed nearly 3,000 articles published between 2016 and 2025 using an innovative multi-layered Natural Language Processing (NLP) framework to capture multiple dimensions of stigma, including discriminatory language, emotional framings indicative of prejudices, stereotypes, and the thematic contexts in which ASD-related stigma appears. Overall, results indicate low levels of overt stigma and a gradual shift toward more positive portrayals, with only temporary disruptions during the pandemic. Some stereotypes remain, highlighting the need for ongoing attention to ASD representation in the media.

Since the advent of Large Language Models (LLMs), much work has been devoted to comparing the linguistic abilities of humans and machines. Figurative language, which is known to rely on pragmatic inferential processes as well as lexical-semantic, sensorimotor, and socio-cognitive information, has been often used as a benchmark for this comparison. In the present study, we build on previous behavioral evidence showing that both distributional and sensorimotor variables come into play when people are asked to produce novel and apt metaphors and examine the behavior of LLMs in the same task. We show that, while distributional features still hold a special status, LLMs are insensitive to the sensorimotor aspects of words. This points to the lack of human-like experience-based grounding in LLMs trained on linguistic input only, while offering further support to the multimodality of conceptual knowledge involved in metaphor processes in humans.

Temporal word embeddings have been successfully employed in semantic change research to identify and trace shifts in the meaning of words. In a previous work, we developed an approach to study the diachrony of complex expressions, namely literary metaphors extracted from Italian literary texts. Capitalizing on the evidence that measures of cosine similarity between the two terms of a metaphor approximate human judgments on the difficulty of the expression, we used time-locked measures of similarity to reconstruct the evolution of processing costs of literary metaphors over the past two centuries. In this work, we present a proof-of-concept study testing the crosslinguistic applicability of this approach on a set of 19th-century English literary metaphors. Our results show that metaphors changed as a function of textual genre but not of epoch: cosine similarity between the two terms of literary metaphors is higher in literary compared to nonliterary texts, and this difference is stable across epochs. We show that the difference between genres is affected by the frequency of the metaphor’s vehicle and the stability of the meaning of both topic and vehicle. Overall, the processing costs of English literary metaphors do not differin different time points, but are influenced by the textual genres of language. In a broader perspective, general considerations can be drawn about the history of literary and nonliterary English language and the semantic change of words