Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).

翻译：悬念是叙事中维持读者兴趣、促使其继续阅读的重要手法，然而在计算文学研究中，这一现象迄今未得到充分探讨。本文聚焦于作者构建悬念的要素之一：危险情境。我们构建了一个标注文本语料库，区分7种危险类型，同时对人物恐惧体验的文本片段进行标注（无论危险是否实际存在）。我们开展了面向危险情境自动检测的实验，发现无监督基线方法可提供有价值的检测信号，但更复杂的分析方法仍必不可少。不出所料，危险与恐惧的描述往往高度依赖语境：既包括局部语境（例如仅提及但未实际出现的危险情境），也涉及全局语境（例如在冒险小说中"风暴"体现字面意义，而在言情小说中则为隐喻用法）。