The spread of misinformation has prompted extensive research on machine-learning–based fake news detection. However, existing datasets differ substantially in content distributions and annotation policies, complicating fair evaluation and generalization assessment. We refer to these structural differences as dataset bias. In this study, we quantitatively analyze dataset bias across multiple public fake news datasets (Kaggle, FNN, ISOT, and NELA-GT-2019/2020) with different annotation granularities, including article-level and publisher-level labels. Using document embedding–based similarity analysis and article category distributions, we examine how such biases affect detection performance under in-dataset and cross-dataset evaluation settings. Furthermore, to leverage large-scale but coarse-grained publisher-level data, we compare proxy-label training with a semi-supervised learning approach based on Virtual Adversarial Training (VAT). Our results show that detection performance strongly depends on dataset-specific biases, and that proxy-label training and SSL exhibit complementary, and sometimes opposite, strengths depending on whether the evaluation emphasizes in-dataset performance or cross-dataset generalization. These findings highlight the importance of appropriate training strategies and evaluation protocols when using heterogeneous fake news datasets.

Large language models (LLMs) are increasingly deployed in multicultural settings; however, systematic evaluation of cultural specificity at the sentence level remains underexplored. We propose the Conceptual Cultural Index (CCI), which estimates cultural specificity at the sentence level. CCI is defined as the difference between the generality estimate within the target culture and the average generality estimate across other cultures. This formulation enables users to operationally control the scope of culture via comparison settings and provides interpretability, since the score derives from the underlying generality estimates. We validate CCI on 400 sentences (200 culture-specific and 200 general), and the resulting score distribution exhibits the anticipated pattern: higher for culture-specific sentences and lower for general ones. For binary separability, CCI outperforms direct LLM scoring, yielding more than a 10-point improvement in AUC for models specialized to the target culture. Our code is available at https://github.com/IyatomiLab/CCI.

In image captioning, multiple captions are often provided as ground truths, since a valid caption is not always uniquely determined. Conventional methods randomly select a single caption and treat it as correct, but there have been few effective training methods that utilize multiple given captions. In this paper, we proposed two training technique for making effective use of multiple reference captions: 1) validity-based caption sampling (VBCS), which prioritizes the use of captions that are estimated to be highly valid during training, and 2) weighted caption smoothing (WCS), which applies smoothing only to the relevant words the reference caption to reflect multiple reference captions simultaneously. Experiments show that our proposed methods improve CIDEr by 2.6 points and BLEU4 by 0.9 points from baseline on the MSCOCO dataset.

We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a β-variational auto-encoder (β -VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability.

Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering. These could be eliminated by using character-level features. We propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier (AraDIC), inspired by the work on image-based character embeddings. AraDIC consists of an image-based character encoder and a classifier. They are trained in an end-to-end fashion using the class balanced loss to deal with the long-tailed data distribution problem. To evaluate the effectiveness of AraDIC, we created and published two datasets, the Arabic Wikipedia title (AWT) dataset and the Arabic poetry (AraP) dataset. To the best of our knowledge, this is the first image-based character embedding framework addressing the problem of Arabic text classification. We also present the first deep learning-based text classifier widely evaluated on modern standard Arabic, colloquial Arabic, and Classical Arabic. AraDIC shows performance improvement over classical and deep learning baselines by 12.29% and 23.05% for the micro and macro F-score, respectively.