We have developed a paradigm for measuring any narrative dimension and we provide the software online. We used it to measure the suspense-arc for a text at a high level of granularity (word level or below).
For details, see: Maria Bentz, Maya Cortez-Espinoza, Vesela Simeonova, Tilmann Köppe, Edgar Onea (2023): Measuring Suspense in Real Time: a New Experimental Methodology.
Here, you can get to the free online repository with which you can access, download and help develop the software.
Here is a link to a related software used to collect readers estimations of probabilities of future narrative events.
Below, you see a screenshot of what the user interface of the software looks like.
We have proposed a theory of narrative suspense that – in opposition to most classical approaches – takes the explanatory burden off of content-related notions such as danger, preferences, likelihood and importance. It mainly focuses on a novel erotetic concept that capures the intuition that suspense happens when the consumer (reader) has the recurring impression of "now, it will happen. I can feel it". What we assume as the basic notion of suspense is a relation between a main question that spans the whole narrative and a series of small but relevant micro questions. The relevant relation is a relation of asymmetric dependency: One possible answer to the micro question resolves the main question while the other does not. We call these suspence-inducing micro questions "potential inquiry terminating (micro) questions (PIT(M)Qs)."
For more details, see: Köppe, T., Onea, E. The Nearly Missed Account of Narrative Suspense. In: Frontiers of Narrative Studies 9 (2023), 273-289.
To provide an empirical base for our theory, we gathered a lot of data and provide it online for future research.
To measure how well our theory is able to predict the emerging of suspense, we annotated narrative texts for any questions that emerge during the experience of reading the narrative.
Furthermore we collected suspense intensity data for the stories we were interested in, using our software.
We were able to observe that a model based solely on the PITQ-theory of suspense in a narrative captures a significant proportion of suspense-variation in various narratives at a high level of granularity.
We additionally collected readers' probability ratings for relevant future events in the narrative for the stories we were interested in.