In science, promotional language (’hype’) is increasing and can undermine objective evaluation of evidence, impede research development, and erode trust in science. In this paper, we introduce the task of automatic detection of hype, which we define as hyperbolic or subjective language that authors use to glamorize, promote, embellish, or exaggerate aspects of their research. We propose formalized guidelines for identifying hype language and apply them to annotate a portion of the National Institutes of Health (NIH) grant application corpus. We then evaluate traditional text classifiers and language models on this task, comparing their performance with a human baseline. Our experiments show that formalizing annotation guidelines can help humans reliably annotate candidate hype adjectives and that using our annotated dataset to train machine learning models yields promising results. Our findings highlight the linguistic complexity of the task and the potential need for domain knowledge. While some linguistic works address hype detection, to the best of our knowledge, we are the first to approach it as a natural language processing task. Our annotation guidelines and dataset are available at https://github.com/hype-busters/eacl2026-hype-dataset.

Promotional language, or "hype", is increasingly common in biomedical research reporting. Adjectives such as groundbreaking, robust, and impactful can engage readers but also risk imposing value judgements and undermining objectivity. Detecting and assessing such language requires distinguishing degrees of promotional intensity (e.g., new < novel < groundbreaking < revolutionary), yet no such graded resource exists. We present an intensity-scaled lexicon of 303 promotional adjectives attested in biomedical writing across eight evaluative domains (e.g. IMPORTANCE, NOVELTY, RIGOUR). Ratings were obtained through Best–Worst Scaling (BWS) with human participants evaluating adjectives for promotional strength in the context of scientific research reporting. We refer to this as the Hyplex resource (Hype Lexicon). The ratings show high internal consistency (r = 0.87; 95% CI [0.85, 0.89]) and correlate most strongly with arousal and dominance in the NRC VAD Lexicon, suggesting that promotional intensity aligns more with reader activation and perceptions of assertiveness than simple positivity. We also release an online BWS platform integrated with the R package bwsTools to support intensity-scaling research in other domains.