Story Settings: A Dataset (2023)

Abstract: Understanding the settings of a given story has long been viewed as an essential component of understanding the story at large. This significance is not only underscored in academic literary analysis but also in kindergarten education. However, despite this significance, it has received relatively little attention regarding computational analyses of stories. This paper presents a dataset of 2,302 time period setting labeled works and 6,991 location setting labeled works. This dataset aims to help with Cultural Analytics of literary works but may also aid in time-period-related questions within literary Q\&amp;A systems.

## introduction the setting of a story is the time and place in which the events in the story are purported to occur. understanding the setting of a story is important to understanding the story's composite pieces, such as characters, events, and plot. this significance is even unscored in children's early education with the united states common core standards having setting detection as a key area of english education for kindergartners @xcite . part of the reason for the significance is that settings can enable us to make inferences as varied as customs/practices, technology, and character limitations. the inferences we can make have various levels of granularity depending on our knowledge of the time period or location. apart from such inferences, story settings are advantageous when conducting cultural analytics using literature. one of the reasons is what is called in philosophy the epistemic role of fiction @xcite . stories have a remarkable impact on people's understanding of the world. empirical studies have shown this is the case even when people know the story is fictional @xcite @xcite . this carries particular weight with historical fiction. these studies seem to suggest that it is hard to read or watch "war and peace" without it shaping our view of the actual transpiring of the war of 1812. such epistemic uses of fiction seem to have had large social effects. for instance, the carefully researched 1852 novel "uncle tom's cabin" was said to have a profound effect on the public's negative perception and consequential response to slavery @xcite . however, some works have been said to misrepresent racial relations, such as "gone with the wind"'s portrayal of the civil war @xcite . having a dataset that distinguishes the time period the work was written in and the time period of its setting enables analysis of how truthful the work is in comparison to historical records. it also enables additional literary analysis. for instance, when doing cultural analysis of fictional character presentations, we may analyze not only how victorian authors presented women in their "modernday" novels but also how they presented women of the past. however, despite the value of identifying a story's time period and location, there are currently no large or diverse datasets for this purpose. this paper presents such a dataset. the dataset is available for download under a creative commons attribution 4.0 license at https: //github.com/krittichier/storysettings . this paper is organized as follows: section 2, describes related work that focused on determining time period and location from texts. in section 3, the time period dataset is outlined, including the retrieval process as well as the cleaning, labeling, and baseline classification from the data. section 4, outlines the location dataset construction and classification using simple metrics. section 5, concludes the paper. ## related work there has been some work focused on the time period setting or other temporal aspects of stories. the first narrative q & a dataset @xcite was offered in 2018 to evaluate reading comprehension. of the over 1,500 textual works, only 6% had a time period setting q & a combo and 7.5% had a location setting q & a combo. although there were some that dealt with the setting for a particular event, these also had low representation. there was a publication on the passage of time within fictional works @xcite , where segments of text are labeled by the time of day they take place (i.e., morning, daytime, evening, and night.) similarly, an annotation guideline for temporal aspects was published as part of the santa (systematic analysis of narrative levels through annotation) project. this dataset addresses the issue of how to deal with jumps in the story timeline, such as analepsis (a flashback) or prolepsis (a flashforward). a few projects have focused on time period classification for non-story texts. most of the literature focuses on news data @xcite . the problem with this, as with many other cases of news data's use in natural language processing, is that news data is often written more explicitly than other text of text @xcite ; in this case particularly, temporal aspects are very clear, sometimes down to the hour @xcite . additionally, news text is often much shorter, and therefore its time spans are smaller. another example of non-story detection is evality's 2020 task @xcite . in this task, works were collected about the former prime minister of italy, alcide de gasperi @xcite . in this task, there were 2,759 works that were then split into five different categories for coarse-grained analysis and 11 different ranges for fine-grained analysis. there have also been two attempts at identifying the time period of a text using time series @xcite . these two papers are written by some of the same authors using different approaches. their work differs from this one in that it deals more with the publication time period than the setting time period. the term frequency-based approach they use is not able to draw this distinction and therefore is less suited for the tasks of digital humanities and cultural analytics. ## time period dataset project gutenberg foot_0 is the source of the literary works. project gutenberg is a resource that contains textual works in the public domain. at the time of this, the united states has the copyright set to expire 70 years after the author dies. as of 2019, all works written prior to 1924 are in the public domain. although there are some public domain works that have been recently published, the majority of the works were published before that date. around 80 percent of the works in project gutenberg are in english. of these english texts, 40 percent are fictional texts. to determine the work's fictional status, a combination of loc classification (namely sub-classifications of "p: language and literature", which were reviewed to be literature labels rather than language or literary criticism) and header terms (such as "fiction", "story", and "tale") were used. three primary resources were used for identifying the time period setting of the work. these resources are library of congress subject classification foot_1 , wikipedia api foot_2 , and sparknotes foot_3 . library of congress classifications of works are expert-labeled topics that include setting information. wikipedia has categories of text related to the time period, such as "set in the 1920s" or "set during the civil war." sparknotes consist of expert reviews of works for study purposes. beautifulsoup foot_4 was used to scrape the html sparknotes webpages and retrieve the information about the works. like many issues within machine learning, the difficulty lies in the scarcity of the data, as there were 2,302 works labeled with time periods settings after cleaning. ## location dataset and baseline classification in order to detect location data, loc headings are used, as well as some sparknotes headings. additionally, datasets from simple maps are used for some of the world cities foot_8 , the usa foot_9 and great britain foot_10 . additionally, given the variance in state names, in loc classification, much of the data consists of states which are abbreviated with either standard abbreviations (e.g., "az") or postal abbreviations (e.g., "ariz"). a table containing alternative state names (full and abbreviated) and postal was used. the reason for using these resources is that it enables a more robust part-whole classification than wordnet currently offers. the results for both loc headings and spar-knote location labels were reviewed by hand due to non-locations with the same term. for instance, though battle is a place in england, but many battles took place in england, which is what is most often referred to with the term battle and england in the heading labels. the dataset included 6,962 gathered with loc subjects. 689 of these works are labeled as having more than one location classification. there are 556 headings with identified locations. there are also 75 sparknotes works with location(s), with around 22 having multiple location labels. 46 works are in both the loc headers and sparknotes, resulting in 6,991 works. baseline classification results using location setting were achieved using simple term occurrence metrics. 34.5% had the setting location as the most often mentioned location. 60.5% had the setting location (or the larger location it falls within, such as the country) as mentioned. the remaining 5% did not have any terms to indicate the location, and it remains an open question what content in the stories the annotators relied on in assigning the label. this dataset covers a more simple version of location setting, namely geolocation. other important features for location are whether it takes place in a house or, better yet, a certain character's house. however, this more nuanced version is only reflected in a few of the sparknotes labels we see, with most having simple geolocation (e.g., country, city, state), which indicates a need for even simple location setting labels. the dataset for the location settings is similar to the the one for time period described in section 3.5. it has a zipped folder of the works and a table that includes all of the same project gutenberg information. however, instead of each work having a location label column, there is a list of location-specific headings. these headings can be used as keys in the accompanying dictionary. each key has an associated country and may also have a city and/or state based on the granularity of the label. ## conclusion this paper presents a dataset of 2,302 time period setting labeled works and 6,991 location setting labeled works. the aim is for these to help with the detection of settings within stories and interesting cultural analytic findings by enabling analysis of cross-time-period writing and the role settings serve for story understanding. it can also help offer refinement/investigation into literary q&a systems. additionally, this project serves as a way to investigate how beneficial metadata on project gutenberg or from loc can be. the aim is that this will enable the use of the loc classifications, which, to our knowledge, have not been capitalized on in natural language processing, at least at this scale or for this aim. there is also room for tracking more carefully where different portions of the work take place as can be seen to be important in the sparknotes' labeling.