Book Review: Data and Doctor Doom: An Empirical Approach to Transmedia Characters by Mark Hibbett

 reviewed by Chris York

Mark Hibbett. Data and Doctor Doom: An Empirical Approach to Transmedia Characters. Palgrave Macmillan, 2024. $110 (Hardcover). https://link.springer.com/book/10.1007/978-3-031-45173-7

Mark Hibbett’s book is the most recent installment of the Palgrave Studies in Comics and Graphic Novels series edited by Roger Sabin. The series has a broad, international focus, with a mission to explore “all aspects of the comic strip, comic book, and graphic novel, […] through clear and informative texts offering expansive coverage and theoretical sophistication,” (ii) and Hibbett’s empirical study delivers on that mission.

Hibbett’s purpose, as he states in his introduction, is “to define a straightforward methodology for empirically analyzing transmedia characters” (1). By identifying and collecting character data in a number of categories from a corpus of texts, researchers should be able to, among other things, analyze character development over time, recognize shifts across media, and empirically identify the core signifiers of a character.

As such, the largest section of the study is the methodology which addresses both the design and implementation of his model for transmedia characters. Though Doctor Doom features in the title of the book, he is simply the primary case study Hibbett uses to illustrate the usefulness of the tool; a thorough analysis of the character is secondary to the explication of the model.

Data-driven analysis is trending within Comics Studies and Hibbett’s intention is to contribute to “database-led methods of corpus analysis” is two ways. First, he is trying to develop a system of identifying and analyzing character-specific signifiers that is adaptable across Comics Studies, and not merely applicable to a single character or storyworld (i.e. a fictional universe in which a story exists, such as the Marvel Universe or Marvel Cinematic Universe). Second, and perhaps more challenging, he is trying to develop a system that is effective for collecting data that is not text specific and, therefore, can draw data effectively for characters and story worlds that exist across different media.

To these ends, the model for transmedia characters records thirteen different kinds of information related to a character, each of which falls into one of four categories: character, behavior, storyworld, and authorship. Character components include appearance, names and titles, physical actions, and dialogue. Behavioral components include perceived behavior, personality traits, and motivations. Storyworld components that were recorded consist of locations, other characters, objects, and previous events. Finally, he identifies references to both textual authors and market authors within the texts.

In creating these components and categories, the author draws from previous attempts to identify essential signifiers for characters and storyworlds. He cites as foundational to his own model the work of Matthew Freeman, Marie Laurie Ryan, Paolo Bertetti, and Roberta Pearson and William Uricchio. In combining elements from all of them, Hibbett believes he has a model that is both practical and comprehensive.

Hibbett is thoughtful in his assignations and provides explanations for how and why he selected the thirteen dimensions for his model. He describes at length, for example, his thought process in constructing his Behavior category. Simply documenting descriptions of a character’s behavior based on language within the text (whether that language comes from the narrator, the featured character, or other characters) is, in his estimation, both inadequate and misleading. Yet, he continues, even a simple description of character behavior by the researcher would risk being neither empirical nor reproducible. He settled, finally, on three components within the Behavior category. “Perceived behavior” and “motivation” rely on language drawn directly from the text. However, the data for “personality traits” is gathered using the 10 Item Short Version of the Big Five Personality Inventory (BFI). The three, in combination, provide a meaningful and objective measurement of character behavior.

The case study he uses to test his model is Marvel’s Doctor Doom from 1961-1987. Hibbett selects Doctor Doom for several reasons; since he is generally not the titular character and appeared in a variety of titles, he “would function as a way of sampling the different Marvel storyworlds over time” (54). Furthermore, since Doctor Doom rarely had his own series, Hibbett argues, there was no specific author or authors who ‘owned’ him, which could provide interesting information regarding what creators saw as the essential signifiers for the character.

Hibbett’s model is largely successful, and the case study of Doctor Doom makes it clear how useful of a tool it can be.  He notes that a primary value of this kind of empirical research is providing some quantitative evidence for some of the conclusions that comics scholars tend to intuit. For instance, scholars of the Marvel Universe would generally conclude that Doctor Doom’s character is, to a large degree, consistent over time; Hibbett’s model provides the data to support that assumption in a number of ways.

However, the model can also reveal inconsistencies and changes in character development. For example, Hibbett observes that Doctor Doom’s use of derogatory exclamations like “Dolt!” and “Clod!” were very common in early representations of the character and a feature that many of the people he surveyed identified as central to Doom’s character. However, Hibbett’s data shows that this kind of language diminished over the decades. He speculates; “[i]t could be that such words were used more often in the earlier period because writers then tended to use dialogue as a way to define character more than those in later periods, where other methods such as appearance and actions became more important. It could also indicate a change in writing style…“ (130). While this is an interesting line of inquiry and one worthy of being pursued, Hibbett does not elaborate further. His purpose is not to argue why certain elements of Doom’s character change or do not change. Rather, the goal of this project is to illustrate the effectiveness of his model by identifying these shifts in character.

There are some shortcomings to his case study, which he readily recognizes. One problem is related to the sample size. Because he was working independently and without funding for the project, he catalogued neither the entirety of Doctor Doom’s appearances during this era, nor did he manage a statistically significant sample size. Rather, he looked only at a “representative” sample of texts, chosen randomly from Doctor Doom’s appearances during the Silver and Bronze ages. These problems of time and cost are likely to persist for anyone wanting to use Hibbett’s model. Furthermore, Doctor Doom’s appearances in media other than comic books are very limited during this era, and so the efficacy of this tool across media is unclear.

In an attempt to illustrate the adaptability of the model, Hibbett includes a chapter in which he uses his character model to compare British and American versions of Denis the Menace. The inclusion of this chapter is my only real criticism of the book. The chapter itself is interesting but would have worked better as a separate document. Here, it seems extraneous, given the almost exclusive attention to Doctor Doom throughout the rest of the book. Hibbett, in fact, pays almost no attention to this chapter in either his discussion or conclusion.

That criticism aside, Hibbett has done some very good work. He also shares his data readily. Through appendices he provides both the corpus he used for Doctor Doom and the survey he used for generating his signifier set. Furthermore, he provides full digital access to the complete data for the Doctor Doom study. I very much recommend this book. The model is a useful analytical tool and Hibbett’s thorough explanation of his process will be invaluable for anyone considering data-driven analysis.