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Easy-to-Understand (E2U) language varieties have been recognized by the UN Convention on the Rights of Persons with Disabilities as a means to prevent communicative exclusion of those facing cognitive barriers and guarantee the fundamental right to Accessible Communication. However, guidance on what it is that makes language ‘easier to understand’ is still fragmented and vague, leading practitioners to rely on their individual expertise. For this reason, this article presents a quantitative corpus analysis to further understand which features of E2U language can more effectively improve verbal comprehension according to professional practice. This is achieved by analysing two parallel corpora of standard and professionally adapted E2U articles to identify adaptation practices implemented according to, in spite of or in addition to official E2U guidelines (Deleanu et al., 2024). The results stemming from the corpus analysis, provide insight into the most effective adaptation strategies that can reduce complexity in verbal discourse. This article will present the methods and results of the corpus analysis.

Digital inclusion increasingly supports adults with intellectual disabilities (ID) to participate online, yet social media posts can be difficult to understand, particularly when they contain strong emotions, slang, or non-standard writing. This paper investigates whether large language models (LLMs) can simplify social media texts to improve cognitive accessibility and preserve emotional meaning. Using an accessibility-oriented prompt based on existing guidance, posts are simplified and emotion preservation is assessed. The results suggest that many simplified posts retain the same emotions, though changes occur, especially when emotions are weakly expressed or ambiguous. Qualitative analysis shows that simplification improves fluency and structure but can also shift perceived emotion through changes to tone, formatting, and other affective cues common in social media text. The research has also revealed that different LLMs produce very different outputs.

This paper summarises the submissions of our team to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification, which aims to create text simplifications that balance reduced linguistic complexity, meaning preservation, and fluency while meeting a predefined target readability level. In this work, we proposed two different methods for CEFR-controlled text simplification: a setup which employed reinforcement fine-tuning of large language models (LLMs) and a conservative lexical pipeline which relied on prompting LLMs to simplify sentences.