Recent social-media analytics research has explored the complex domain of slogans and product or service endorsements, which present classification challenges in marketing, owing to their adaptability across different contexts. Existing research emphasizes flat-text classification, neglecting the nuanced hierarchical structure of English at the document and sentence levels. To overcome this gap, this study introduces a robust slogan identification and classification (RoICS) model within a ubiquitous-learning framework. It uses a new dataset that includes 6,909 ProText and 1,645 propaganda-text corpora (PTC) samples, encompassing both slogan and non-slogan labels. This model investigates the complex hierarchical multilabel structure of slogans using a granular computing–based deep-learning model and fine-grained structures. The proposed RoICS model achieved an accuracy of 84%, outperforming state-of-the-art models. We validated the utility of our contributions through a series of quantitative and qualitative experiments across various openness scenarios (25%, 50%, and 75%) using the ProText and PTC datasets. These findings not only refine our understanding of slogan detection, but also hold significant implications for information-systems researchers and practitioners, offering a potent tool for sentence-level ubiquitous-learning data analysis.