We present a comprehensive evaluation of gender fairness in large language models (LLMs), focusing on their ability to handle both binary and non-binary genders. While previous studies primarily focus on binary gender distinctions, we introduce the Gender Inclusivity Fairness Index (GIFI), a novel and comprehensive metric that quantifies the diverse gender inclusivity of LLMs. GIFI consists of a wide range of evaluations at different levels, from simply probing the model with respect to provided gender pronouns to testing various aspects of model generation and cognitive behaviors under different gender assumptions, revealing biases associated with varying gender identifiers.We conduct extensive evaluations with GIFI on 20 prominent open-source and proprietary LLMs of varying sizes and capabilities, discovering significant variations in LLMs’ gender inclusivity. Our study highlights the importance of improving LLMs’ inclusivity, providing a critical benchmark for future advancements in gender fairness in generative models.

TikTok has emerged as a key platform for discussing polarizing topics, including climate change. Despite its growing influence, there is limited research exploring how content features shape emotional alignment between video creators and audience comments, as well as their impact on user engagement. Using a combination of pretrained and fine-tuned textual and visual models, we analyzed 7,110 TikTok videos related to climate change, focusing on content features such as semantic clustering of video transcriptions, visual elements, tonal shifts, and detected emotions. (1) Our findings reveal that positive emotions and videos featuring factual content or vivid environmental visuals exhibit stronger emotional alignment. Furthermore, emotional intensity and tonal coherence in video speech are significant predictors of higher engagement levels, offering new insights into the dynamics of climate change communication on social media. (2) Our preference learning analysis reveals that comment emotions play a dominant role in predicting video shareability, with both positive and negative emotional responses acting as key drivers of content diffusion. We conclude that user engagement—particularly emotional discourse in comments—significantly shapes climate change content shareability.