The frequent introduction of new emojis in each Unicode release creates a dynamic shift in social media content, providing a unique opportunity to explore the evolution of digital language. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain popularity and evolve in meaning. We find that the community size of early adopters and emoji semantics are positively correlated with their popularity. Certain emojis experienced notable shifts in the meanings and sentiment associations during the diffusion process. Additionally, we propose a novel framework utilizing language models to extract words and pre-existing emojis with semantically similar contexts, which enhances the interpretation of new emojis. The framework demonstrates its effectiveness in improving downstream text classification performance by substituting unknown new emojis with familiar ones. This study offers a new perspective in understanding how new language units are adopted, adapted, and integrated into the fabric of online communication.

Large language models (LLMs) hold promise for automating causal knowledge extraction, a fundamental component of scientific discovery, yet their ability to reliably identify causal claims in scientific literature remains poorly understood. Using 1,000 manually annotated abstracts spanning 20 disciplines, we evaluate eight frontier LLMs under four prompting strategies on a causal conclusion identification task. More elaborate prompting strategies do not necessarily improve performance, while open-weight models achieve competitive performance relative to proprietary models. LLMs systematically produce false-positive causal predictions on non-causal abstracts, with GPT-5 showing the most extreme bias, and Few-Shot prompting provides the most consistent calibration benefit. Performance varies substantially across disciplines with stronger and more stable results in biomedical fields. Our findings provide practical insights for using LLMs in scientific causal analysis and highlight calibration and domain variation as key challenges for advancing causal reasoning in scholarly text.