Cinemetrics is a form of data art that opens up new ways of analysing film. According to cinemetrics pioneer Fredric Brodbeck, this new art form \u201cis about measuring and visualising movie data in order to reveal the characteristics of films and to create a visual fingerprint for them\u201d. Brodbeck produces these digital fingerprints by \u201cdisassembling video files into their components [such as] video, audio [and] subtitles\u201d. He then analyses each component for every scene, identifying elements such as average shot length, movement and colour, as well as taking into account a film\u2019s metadata (chapters) while cross-referencing with the IMDb\u2019s source of dialogue from the film. The end result resembles a pie chart, where users are able to look at patterns across the film as whole or inspect further into each segment. Brodbeck creates an interactive and comparative way of exploring cinema that gives its user an insight into the dynamics of any given film. For Brodbeck, cinemetrics enables viewers to assess cinema in its entirety, claiming “nobody has ever seen a movie the way it really is, but always just a partial view [\u2026] it\u2019s hard to capture and display them in their entireness\u201d (qtd. in Pavlus). While data analysis may appear objective in the quantification of the cinematic art, Brodbeck\u2019s approach also brings forth subjective aspects of dimension and perception that help users recognise what is typical or distinct across a range of films. Cinemetrics provides a means to step back from a film and reveal what might otherwise remain unnoticed.<\/p>\n
The more generalised term for Brodbeck\u2019s presentation is data visualisation, which is the process of creating visualised patterns and trends to enable analysis. Data visualisation can help film audiences make more detailed and informed choices based on a film\u2019s colour scheme, level of motion or amount of dialogue. Users can compare a number of films to others they are familiar with. Asking, for example, if the film offers the same pastel colour palette of a Wes Anderson film. As Brodbeck claims, users are able to sort between different types of science fiction films and make more detailed decisions not only based on a film\u2019s genre. For example, 2001: A Space Odyssey<\/em> (1968) Alien<\/em> (1979) and Solaris<\/em> (2002) appear side by side showing how even though the films\u2019 colour schemes remain similarly dark and blue toned, their level of action varies, with the hammering spikes of Alien <\/em>differing greatly to the slower pulses of Solaris. <\/em>Cinemetrics can be used to \u201cfind out if the data [\u2026] inherent in the movie can be used to make something visible that otherwise would remain unnoticed\u201d, the film itself becomes \u201ca source of data\u201d (Brodbeck). The viewer\u2019s choice becomes based more on sense and perception, less intertwined with what is recommended from critics or user reviews on aggregator websites.<\/p>\n Data visualisation can also be used to analyse and critique general trends, providing an evidence-based means of uncovering certain industry characteristics. Data-journalist and information designer David McCandless offers scene-by-scene breakdowns of Hollywood films. The infographic Based on a *True* True Story? <\/em>presents audiences with a \u201cbeat-by-beat [\u2026] test\u201d of a film\u2019s \u201cveracity on a data level\u201d. As Selma<\/em> (2015) scores 100% for veracity against American Sniper<\/em>\u2019s (2015) 56.9% this indicates how data visualisation, for McCandless, \u201ccut[s] through BS and fake news [\u2026] reveal[ing] the hidden connections, patterns and stories underneath\u201d. The Hollywood In$ider<\/em> is an infographic that examines over 1000 major Hollywood films between 2008-2016. The infographic is a traditional scattergraph, but utilises an interactive aspect to encourage users to \u201cfilter, sort, plot and delve\u201d into its data (McCandless). The visualisation enables users to find out which films \u201crecouped the most of their budget [and] divided audiences and split the critics\u201d, effectively \u201cstar[ing] into the underbelly of the Hollywood machine\u201d (McCandless). The infographic collates data from BoxOfficeMojo, TheNumbers, RottenTomatoes and Metacritic and allows viewers to compare films by their genres, year released and script type, McCandless\u2019 visualisation navigates through a dense amount of information to provide a more functional information map.<\/p>\n Hanah Anderson and Matt Daniels produced a visualisation called Film Dialogue<\/em> from approximately 2,000 screenplays, broken down by gender and age. They explain their project as being \u201cborn out of the less-than-stellar response\u201d to their previous infographic Hollywood<\/em>\u2019<\/em>s Gender Divide and its Effect on Films <\/em>which analysed films that failed the Bechdel Test. They sorted the 200 highest grossing films between 1995-2015 into three categories: films with an all-male writing team, films with at least one woman writer and films with all women writers. Then they looked at how many passed and failed the Bechdel Test in each category. The results of Gender Divide <\/em>showed that by having even one woman on the writing team improved the odds of passing the Bechdel Test. The team behind the visualisation believed it revealed the problem surrounding Hollywood\u2019s diversity problem, claiming the issue lies in not what is on the screen but also behind it. They speculated that \u201cfilmmakers, unintentionally, make movies about themselves\u201d and \u201csince the most powerful producers, writers, and directors are men, male-themes permeate into Hollywood\u2019s output\u201d (Blinderman). Although their infographics do not take account of dialogue as it appears in the completed film (including screen time or the context of how a character is portrayed) Anderson and Daniels argue \u201cwe have to assume that screenplays, across a large sample, are more or less representative of the final product\u201d and \u201cby measuring dialogue, we have much more objective view of gender in film\u201d. Their visualisation aims to evidence statements about the industry\u2019s lack of diversity and this is especially significant in relation to recent campaigns such as #OscarsSoWhite.<\/p>\n Data visualisation can also be of interest to the film industry. It can be used throughout the process of filmmaking, from pre- to post-production, as well as after a film\u2019s release. Former Lucasfilm Chief Technology Officer, Dave Story, while discussing Hollywood\u2019s previous approach to big data, observes that \u201centertainment companies have always had data \u2013 the trouble is that much of it has been backwards-looking\u201d, relying too much on previous trends (Cox). In contrast, data visualisation now enables the industry to make more informed and cost-effective choices in relation to the films they are yet to produce. Story notes how films such as The Avengers <\/em>(2012), Gravity <\/em>(2013) and Captain America 2: The Winter Soldier <\/em>(2014) all utilised pre-visualisation of \u201ccomplex scenes and effects\u201d (Cox). The Avengers <\/em>used pre-visualisation for location logistics, including \u201cplotting out a virtual clock to determine best times of day to shoot\u201d (Sarto), as well as for technical measurements such as props, stunts and CG performances. Story also states that companies are now \u201cdoing visualisations of their data\u201d, working out \u201cthe costs and data flows for all the pixels [\u2026] put on [a film\u2019s] movie screen\u201d (Cox). Brodbeck formulates, what he terms, \u201cmovie barcodes\u201d after a film has been created. Yet, by producing these barcodes before films are made, companies are able to use graphics and visualisation to already for logistical planning of the production of large amounts of data.<\/p>\n According to User Experience designer Jeff Soyk, who was also part of the Peabody-winning, web-based documentary Hollow <\/em>(2013), \u201c[data visualisation] can not only act as supporting content but can drive the overall story\u201d (Astle). Hollow <\/em>focuses on the lives of residents in the small town of McDowell County, West Virginia. The film, as an interactive documentary and community participatory project, utilised transmedia storytelling to reflect the varying stories of the county\u2019s residents. Data visualisation drove parts of Hollow <\/em>as users were able to manipulate the graphics and navigate through stories of unemployment, drug use or health issues. While working with data visualisation for Hollow<\/em>, Soyk states \u201cthe challenge in generating these graphics is contextualising them, designing them and making sure they play an appropriate role in propelling the story forward\u201d (Astle). As such, data visualisation becomes integral in the film itself, not only aiding with industry pre-visualisation, but also in pre-conceiving a cinematic universe. Brodbeck\u2019s own barcodes and fingerprints becoming stories within themselves.<\/p>\n In contrast to this optimistic view, Johanna Drucker asserts that \u201cevery single visualisation being used in the digital humanities is visually impoverished, conceptually corrupted and intellectually inadequate to the tasks of humanistic work\u201d as they rarely exhibit the true extent of ambiguity, heterogeneity and contradiction within film data (Drucker 2016A). Drucker argues that \u201cthere are no transparent visualisations [\u2026] they are all acts of interpretation with assumptions that are encoded in their form\u201d, therefore, \u201cwe need to pay attention to and engage with [the] distortions\u201d of data visualisation (Drucker 2016B). For Drucker, the question remains, can critics and scholars in the humanities adopt visualisation methods from other disciplines and the industry and use these to make full, meaningful interpretations of a particular film? It is not enough to accept data as it is in a graph or diagram, there is a need to question who made it and why, as well as ask which value system it is based on.<\/p>\n Like Drucker, Papagena Robbins believes information is never simply information because it is also shaped by ideology. In response to theorist Marc Furstenau’s uncertainty of the value of data visualisation at the 2011 Impact International Conference, Robbins asserted that film-making should not subject to the imposition of quantitative methods. Robbins is wary of the use of such \u201cquantification projects\u201d to \u201ccreate the \u2018perfect film formula\u2019 [\u2026] which can be used in a totalising manner by business executives to limit which scripts get produced and which do not in the future\u201d. Data visualisations, for Robbins, must be always be scrutinised as much as the film itself. Matthew Ogonoski also has reservations and asks, \u201cis it worth constructing the graph only to find what one was looking for [\u2026] why would one bother spending the time to compile a graph simply to start with the graph as the object of study?\u201d. Ogonoski queries the value of such research and its \u201cludicrously frivolous findings\u201d, viewing cinemetrics as rhetorical and adding little to the discipline of film studies. In order to explore cinema, he argues, it is \u201cinfinitely more satisfying\u201d to begin with the film itself.<\/p>\n Perhaps data visualisation has yet to rise to Drucker\u2019s challenge as the question of how to represent. Cinemetrics, however, does provide us with innovative ways of analysing film that are useful to audiences, critics and filmmakers. Rather than focus on the difference between box office gross or the critical reviews of Andrei Tarkovskii\u2019s Solaryis<\/em> (1972) and Steven Soderbergh\u2019s Solaris<\/em> (2002), Brodbeck\u2019s approach uses \u201cthe movie itself as a source of data\u201d, looking at \u201cwhat sort of information can be extracted from it\u201d and creating a more dynamic and interactive graphic representation of these two films side by side. The result is illuminating and provocative, even if we don\u2019t yet know quite what to do with the what is revealed.<\/p>\n References<\/strong><\/p>\n Anderson, Hanah and Daniels, Matt. \u201cFilm Dialogue\u201d pudding.cool<\/em>, n.d. Web. 9 Mar. 2017.<\/p>\n Astle, Randy. \u00a0\u201cCharting the Course: Data Visualization in Documentary Film\u201d. Filmmakermagazine.com<\/em>, 20 Oct. 2014. Web. 9 Mar. 2017.<\/p>\n Blinderman, Ilia, Daniels, Matt and Friedman, Lyle. \u201cHollywood’s Gender Divide and its Effect on Films\u201d polygraph.cool<\/em>, n.d. Web. 9 Mar. 2017.<\/p>\n Brodbeck, Frederic. \u201cCinemetrics\u201d. Cinemetrics.com<\/em>, n.d. Web. 9 Mar. 2017<\/p>\n Cox, Ryan. \u201cHow Hollywood is Reshaping Big Data visualisation\u201d. Siliconangle.com<\/em>, 6 May 2014. Web. 9 Mar. 2017.<\/p>\n Drucker, Johanna. \u201cGraphic Provocations: What Do Digital Humanists Want From Visualization?\u201d Sir David Davies Lecture Theatre, London. 25 May 2016. Lecture.<\/p>\n Drucker, Johanna. \u201cNew Dimensions in Humanities Visualization\u201d University of Hamburg, Hamburg. 6 Jun. 2016. Lecture.<\/p>\n McCandless, David. \u201cBased on a *True* True Story?\u201d. Informationisbeautiful.net, <\/em>n.d. Web. 9 Mar. 2017.<\/p>\n McCandless, David. \u201cThe Hollywood In$ider\u201d. Informationisbeautiful.net, <\/em>n.d. Web. 9 Mar. 2017.<\/p>\n McCandless, David. \u201cThe Beauty of Data Visualisation\u201d. TED. July 2010. Lecture<\/p>\n Ogonoski, Matthew. \u201cFurstenau and the problem of Cinemetrics\u201d. Arthemis-cinema.ca<\/em>, n.d. Web. 9 Mar. 2017.<\/p>\n Pavlus, John. \u201cInfographic Of The Day: Taking The “Visual Fingerprints” Of Famous Films\u201d. Fastcodesign.com<\/em>, 27 Sept. 2011. Web. 9 Mar. 2017<\/p>\n Robbins, Papagena. \u201cPolemics and Disciplinary Questioning in Marc Furstenau’s Talk on (the style of) \u2018Cinemetrics\u2019\u201d. Arthemis-cinema.ca<\/em>, n.d. Web. 9 Mar. 2017.<\/p>\n Sarto, Dan. \u201cThe Third Floor Helps Visualize \u2018Avengers: Age of Ultron<\/em>\u2019<\/em>\u201d. Awn.com, <\/em>10 Jun. 2015. Web. 9 Mar. 2017.<\/p>\n Written by Katy Thompson (2017); Queen Mary, University of London <\/strong><\/p>\n This article may be used free of charge. Please obtain permission before redistributing. Selling without prior written consent is prohibited. In all cases this notice must remain intact.<\/p>\n","protected":false},"excerpt":{"rendered":" Cinemetrics is a form of data art that opens up … Continue reading