Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language Models (LLMs), increasingly mediate interactions on web platforms, the risk that these systems perpetuate societal biases through their representation of such symbols is a significant concern. This paper presents the first large-scale comparative study of bias in skin-toned emoji representations across two distinct model classes. We systematically evaluate dedicated emoji embedding models (emoji2vec, emoji-sw2v) against four modern LLMs (Llama, Gemma, Qwen, and Mistral). Our analysis first reveals a critical performance gap: while LLMs demonstrate robust support for skin tone modifiers, widely-used specialized emoji models exhibit severe deficiencies. More importantly, a multi-faceted investigation into semantic consistency, representational similarity, sentiment polarity, and core biases uncovers systemic disparities. We find evidence of skewed sentiment and inconsistent meanings associated with emojis across different skin tones, highlighting latent biases within these foundational models. Our findings underscore the urgent need for developers and platforms to audit and mitigate these representational harms, ensuring that AI's role on the web promotes genuine equity rather than reinforcing societal biases.

This paper presents a publicly accessible large-scale geo-tagged Twitter dataset, comprising 95.8 million tweets from 247 countries, spanning from Jan. 2012 to Dec. 2021. To systematically extract this dataset from over 57.18 TB of raw tweets, we employed parallel computing on a 40-node cluster with 480 CPU cores. Distinguishing it from most existing Twitter datasets, our dataset includes four-level granularity tweet locations, two-level granularity user profile locations, and tweet text languages, enabling personalized queries. To enhance the open accessibility of our dataset, we have designed an innovative interactive online query system (https://sigspatial.yunhefeng.me) and provided free-to-use JSON APIs (https://github.com/ResponsibleAILab/unt-geotweet-api) for customized queries to retrieve tweet IDs in tweet coordinate, tweet text-based location, and user location modes. Then users can use https://github.com/ResponsibleAILab/unt-tweet-rehydration to download complete tweet information. Furthermore, we have demonstrated the practical utility of our dataset through two applications: human movement modeling and geo-aware Large Language Model (LLM) tuning. Our geo-tagged Twitter dataset, along with the accompanying query system and APIs, contributes to the research community and opens up avenues for multidisciplinary investigations and the advancement of knowledge.

Many information access and machine learning systems, including recommender systems, lack transparency and accountability. High-quality recommendation explanations are of great significance to enhance the transparency and interpretability of such systems. However, evaluating the quality of recommendation explanations is still challenging due to the lack of human-annotated data and benchmarks. In this paper, we present a large explanation dataset named RecoExp, which contains thousands of crowdsourced ratings of perceived quality in explaining recommendations. To measure explainability in a comprehensive and interpretable manner, we propose ExpScore, a novel machine learning-based metric that incorporates the definition of explainability from various perspectives (e.g., relevance, readability, subjectivity, and sentiment polarity). Experiments demonstrate that ExpScore not only vastly outperforms existing metrics and but also keeps itself explainable. Both the RecoExp dataset and open-source implementation of ExpScore will be released for the whole community. These resources and our findings can serve as forces of public good for scholars as well as recommender systems users.

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. Training wide and deep neural networks require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger models or to accelerate training. Our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.

As emojis become prevalent in personal communications, people are always looking for new, interesting emojis to express emotions, show attitudes, or simply visualize texts. In this study, we collected more than thirty million tweets mentioning the word “emoji” in a one-year period to study emoji requests on Twitter. First, we filtered out bot-generated tweets and extracted emoji requests from the raw tweets using a comprehensive list of linguistic patterns. Then, we examined patterns of new emoji requests by exploring their time, locations, and context. Finally, we summarized users' advocacy behaviors and identified expressions of equity, diversity, and fairness issues due to unreleased but expected emojis, and concluded the significance of new emojis on society. To the best of our knowledge, this paper is the first to conduct a systematic, large-scale study on new emoji requests.