Measuring Information Propagation in Literary Social Networks (2020)

Abstract: We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text. We describe a new pipeline for measuring information propagation in this domain and publish a new dataset for speaker attribution, enabling the evaluation of an important component of this pipeline on a wider range of literary texts than previously studied. Using this pipeline, we analyze the dynamics of information propagation in over 5,000 works of fiction, finding that information flows through characters that fill structural holes connecting different communities, and that characters who are women are depicted as filling this role much more frequently than characters who are men.

## introduction with the rise of sociological approaches to narrative, work in literary criticism has increasingly turned to the ways in which authors depict social networks in their texts. this includes critical attention to both network topologies, such as understanding characters and their structural relationships with others @xcite , and information flow, such as theorizing the representation of disease and gossip @xcite @xcite . much computational work in nlp has arisen to support the former line of research, including extracting social networks from text @xcite , predicting familial relationships @xcite , and modeling the interactions between characters @xcite . this in turn has driven work in the digital humanities examining the structure of literary networks @xcite @xcite . at the same time, however, there remains a substantial gap in computational work to support the "miss havisham is dead" "she died" figure 1 : the character co-occurrence network for great expectations. nodes represent characters and edges represent conversational interactions. below the network, we illustrate an example of information transmission across a character triad. in this work, we specifically aim to fill this gap by developing methods to track the flow of information in novels by extracting instances of a message passing from character a to character b to character c, only given a depiction of their conversational interactions. we develop a methodology for modeling this mode of propagation in both explicit networks (where one character provides information that is explicitly attributed to another character, such as "bob told me that jack escaped"); and in implicit networks, where information is repeated by multiple characters without such attribution. while the results of the methods enable a range of potential analyses-for instance, comparative analysis between authors, characters, and dyads-we focus on two illustrative case studies. first, we examine the linchpins of information flow-the characters who are most responsible for the propagation of information-and how they are positioned relative to the overall network topology; and second, we examine the gender dynamics of information propagation and what it tells us about how novelists represent men and women as the means and agents for transmitting facts, gossip, and other details about the social workings of these imagined worlds. we make the following contributions with this work: ## related work much of the computational research into information propagation and diffusion has focused on the domain of social media @xcite . research in this area includes analyses of information diffusion in blogs @xcite , the spread of news across online networks @xcite , and in particular, the spread of rumor and misinformation @xcite @xcite . a core aspect of this work that strongly differs from networks in fiction is that the individual components of social media networks (the nodes, edges, and instances of propagation) are often directly observed. in modeling retweet dynamics in twitter, for instance, nodes are defined as unique users, edges are directly observed friend and follow links defined by the platform, and propagation occurs when one user retweets a message posted by another they are connected to. more closely related to the challenges posed by detecting propagation in fiction is work that may directly observe the node and edge structure of a network, but must infer an act of propagation, including work in tracking the diffusion of memes @xcite ), text reuse across legislative bills @xcite and quotations in news @xcite . while information propagation has yet to inform work in narrative (hence the purpose of this study), network structure has increasingly informed literary scholarship. following the work of @xcite , literary scholars have in recent years begun to explore the role that social networks play both in authorial composition @xcite and in the narrative representation of "networked social experience" @xcite . treating literary works themselves as networks, however, poses distinct computational challenges. while research into information propagation in social media tends to presume access to explicit networks, the character networks represented in novels are implicit. to determine these networks, we draw on previous work by @xcite , who build edges between character nodes through conversational interactions. various computational work to extract social networks from literature has built on this research over the past ten years, foot_0 including fundamental methods designed to extract networks for other languages like german @xcite , incorporate other categories of nodes such as locations @xcite and objects @xcite , and analyze the structure of networks to test specific hypotheses @xcite @xcite . our work builds on this tradition by introducing methods to reason about the phenomenon of propagation in fiction based on these constructed networks. ## methods our goal in this work is to investigate the behavior of information propagation in literary texts. in order to identify acts of propagation in this context, we need to determine the underlying network structure of a novel, including the nodes (by inferring characters) and the edges (by inferring some interaction between them). we describe first our pipeline for doing so, which involves identifying a set of unique characters from their mention in a text using coreference resolution ( §3.1), attributing dialogue to those characters ( §3.2), building a social network of speakers and listeners from that data ( §3.3), and operationalizing a measure of "information" that we can treat as an atomic unit involved in propagation using slot-based information extraction ( §3.4). with these constructed networks, we can measure acts of implicit propagation ( §3.5) and explicit propagation ( §3.6) within it. ## experiments given instances of information propagation extracted from novels, we seek to understand the structural roles of the literary network and its topography that contribute to information passing between dyads. in particular, we seek to disentangle two possible alternatives: h1. information propagates through bridges who pass information between otherwise disconnected communities. h2. information propagates among densely connected strong ties (such as between members of the same family who interact frequently). these alternatives correspond to different functions of gossip in literature, as theorized by @xcite : while gossip primarily involves the "deliberate circulation of information," it also functions to reinforce existing relationships among strong ties (a point taken up in real-world social networks in foster ( 2004 )). we operationalize this distinction for understanding the dynamics of implicit propagation by describing information-propagating characters and non-information-propagating characters through six network measures that capture their topological properties within the network: we use the above measures to describe all nodes that either function as the b node in an @xmath0, or could function as such a node. more specifically, whenever we observe an instance of propagation @xmath1, in which at least one separate character b was co-present with b when hearing a's information (but did not propagate it further), we select a pair comprised of b as a propagating node and b as a non-propagating node (sampling b from the set of co-present characters if more than one was present). in cases in which no non-propagating character was co-present, we instead sample a b from the set of propagating instances that had multiple co-present characters. when sampling the non-propagating b nodes, we only select characters that have been observed to speak at least once in the text based on our speaker attribution model (we hypothesize that selecting these characters is a better way to judge the efficacy of a propagation model, since they at least vocalize some information in the narrative, and hence are more likely to resemble propagating nodes in terms of their narrative functions). ## conclusion we introduce the task of identifying information propagation in literary social networks, designing an nlp pipeline for extracting both implicit and explicit propagation. this work offers a new perspective on the analysis of social networks in literary texts by considering the dynamics of how information flows through them-both as a result of the structural topology of the network (characters who successfully propagate are information bridges between communities), and as a result of the specific characteristics of each node (women are depicted more frequently as successful propagators than men). this study, of course, contains limitations: readers of fictional works are only afforded a partial perspective of the world that is representednamely the interactions an author chooses to describe (and not, for example, the dialogue we might presume takes place "off-screen"). considered from a narratological perspective, however, this is a benefit rather than a drawback, since our goal is not to understand the underlying reality of these imagined worlds but rather how authors opt to represent the informational dynamics from which their stories are constructed. in developing this pipeline to examine how authors depict the transmission of information within nar-rative texts, we hope to drive a variety of future research in this space, including not only such narratological questions as how "gossip impels plots" @xcite , but also questions pertaining to issues of bias in representation, the flow of information, and factuality. code to support this work can be found at https://github.com/mbwsims/ literary-information-propagation .