1. Home
  2. Issues
  3. Volume 21, Issue 1 (2023)
  4. Motion Picture Editing as a Hawkes Proce ...

Journal of Data Science


Motion Picture Editing as a Hawkes Process
Volume 21, Issue 1 (2023), pp. 43–56
Pub. online: 7 July 2022      Type: Data Science In Action      Open accessOpen Access
Received
1 April 2022
Accepted
20 June 2022
Published
7 July 2022

Abstract

In this article I analyse motion picture editing as a point process to explore the temporal structure in the timings of cuts in motion pictures, modelling the editing in 134 Hollywood films released between 1935 and 2005 as a Hawkes process with an exponential kernel. The results show that the editing in Hollywood films can be modelled as a Hawkes process and that the conditional intensity function provides a direct description of the instantaneous cutting rate of a film, revealing the structure of a film’s editing at a range of scales. The parameters of the exponential kernel show a clear trend over time to a more rapid editing style with an increase in the rate of exogenous events and small increase in the rate of endogenous events. This is consistent with the shift from a classical to an intensified continuity editing style. There are, however, few differences between genres indicating the consistency of editing practices in Hollywood cinema over time and different types of films.

Supplementary material

 Supplementary Material
The complete set of results and plots for all 134 films in the sample along with the R code used in this project are available for the reader to explore as a shiny app at https://tinyurl.com/2p8c86u3. The data, code, and results for this article are also available on the supporting GitHub repository at DrNickRedfern/hollywood-hawkes.

References

 
Balzarotti S, Cavaletti F, D’Aloia A, Colombo B, Cardani E, Ciceri MR, et al. (2021). The editing density of moving images influences viewers’ time perception: the mediating role of eye movements. Cognitive Science, 45(4): e12969.
 
Bordwell D (2002). Intensified continuity visual style in contemporary American film. Film Quarterly, 55(3): 16–28.
 
Bordwell D (2006). The Way Hollywood Tells It: Story and Style in Modern Movies. University of California Press, Berkeley, CA.
 
Calavita M (2007). “MTV Aesthetics” at the movies: interrogating a film criticism fallacy. Journal of Film and Video, 59(3): 15–31.
 
Cheysson F (2021). hawkesbow: Estimation of Hawkes Processes from Binned Observations. R package version 1.0.2.
 
Cheysson F, Lang G (2021). Strong-mixing rates for Hawkes processes and application to Whittle estimation from count data.
 
Cliff N (1993). Dominance statistics: ordinal analyses to answer ordinal questions. Psychological Bulletin, 114(3): 494–509.
 
Cutting JE (2016a). The evolution of pace in popular movies. Cognitive Research: Principles and Implications, 1(1): 30.
 
Cutting JE (2016b). Narrative theory and the dynamics of popular movies. Psychonomic Bulletin & Review, 23(6): 1713–1743.
 
Cutting JE, Candan A (2015). Shot durations, shot classes, and the increased pace of popular movies. Projections, 9(2): 40–62.
 
Cutting JE, DeLong JE, Brunick KL (2018). Temporal fractals in movies and mind. Cognitive Research: Principles and Implications, 3(1): 8.
 
Cutting JE, DeLong JE, Nothelfer CE (2010). Attention and the evolution of Hollywood film. Psychological Science, 21(3): 432–439.
 
Daley D, Vere-Jones D (2003). An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods. Springer International Publishing, New York, NY.
 
Filimonov V, Sornette D (2015). Apparent criticality and calibration issues in the hawkes self-excited point process model: application to high-frequency financial data. Quantitative Finance, 15(8): 1293–1314.
 
Hawkes AG (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1): 83–90.
 
Hawkes AG (2018). Hawkes processes and their applications to finance: a review. Quantitative Finance, 18(2): 193–198.
 
Johnson SG (n.d.) The NLopt nonlinear-optimization package.
 
Lambert RC, Tuleau-Malot C, Bessaih T, Rivoirard V, Bouret Y, Leresche N, et al. (2018). Reconstructing the functional connectivity of multiple spike trains using Hawkes models. Journal of Neuroscience Methods, 297: 9–21.
 
Lang A, Bolls P, Potter RF, Kawahara K (1999). The effects of production pacing and arousing content on the information processing of television messages. Journal of Broadcasting & Electronic Media, 43(4): 451–475.
 
Lang A, Zhou S, Schwartz N, Bolls PD, Potter RF (2000). The effects of edits on arousal, attention, and memory for television messages: when an edit is an edit can an edit be too much? Journal of Broadcasting & Electronic Media, 44(1): 94–109.
 
Laub PJ, Taimre T LY (2021). The Elements of Hawkes Processes. Springer International Publishing, Cham.
 
Leake M, Davis A, Truong A, Agrawala M (2017). Computational video editing for dialogue-driven scenes. ACM Transactions on Graphics, 36(4): 1–14.
 
Liu DC, Nocedal J (1989). On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1–3): 503–528.
 
Ludwig M, Bertling C (2017). The effect of cutting rates on the liking of live sports broadcasts. International Journal of Sport Communication, 10(3): 359–370.
 
Mei H, Eisner JM (2017). The neural hawkes process: a neurally self-modulating multivariate point process. In: Advances in Neural Information Processing Systems (I Guyon, UV Luxburg, S Bengio, H Wallach, R Fergus, S Vishwanathan, R Garnett, eds.), volume 30. Curran Associates Inc.
 
Ogata Y (1988). Statistical models for earthquake occurrences and residual analysis for point processes. Journal of the American Statistical Association, 83(401): 9–27.
 
Omi T, Hirata Y, Aihara K (2017). Hawkes process model with a time-dependent background rate and its application to high-frequency financial data. Physical Review E, 96: 012303.
 
Pearlman K (2017). Editing and cognition beyond continuity. Projections, 11(2).
 
R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
 
Redfern N (2014). Robust estimation of the mAR index of high grossing films at the US box office, 1935 to 2005. Journal of Data Science, 12(2): 277–291.
 
Redfern N (2015). Exploratory data analysis and film form: the editing structure of Friday the Thirteenth (1980). Post Script: Essays in Film and the Humanities, 34(2–3): 71–83.
 
Redfern N (2020). Modelling shot lengths of Hollywood motion pictures with the Dagum distribution. Statistica, 80: 81–99.
 
Seonwoo Y, Oh A, Park S (2018). Hierarchical Dirichlet Gaussian marked Hawkes process for narrative reconstruction in continuous time domain. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 3316–3325”. Association for Computational Linguistics, Brussels.
 
Thompson K (1999). Storytelling in the New Hollywood. Harvard University Press, Cambridge, MA.
 
Unwin HJT, Routledge I, Flaxman S, Rizoiu MA, Lai S, Cohen J, et al. (2021). Using Hawkes processes to model imported and local malaria cases in near-elimination settings. PLOS Computational Biology, 17(4): e1008830.
 
Wang T, Bebbington M, Harte D (2012). Markov-modulated Hawkes process with stepwise decay. Annals of the Institute of Statistical Mathematics, 64(3): 521–544.
 
Watine P, Bodaghi A, Schmitt KA (2022). An attempt to evaluate information spread using the Hawkes process. In: Proceedings of the Future Technologies Conference (FTC) 2021, Volume 2 (K Arai, ed.), volume 359, 354–362. Springer International Publishing, Cham.
 
Zadeh AH, Sharda R (2015). Hawkes point processes for social media analytics. In: Reshaping Society through Analytics, Collaboration, and Decision Support (LS Iyer, DJ Power, eds.), volume 18, 51–66. Springer International Publishing, Cham.

Related articles PDF XML
Related articles PDF XML

Copyright
2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
computational film analysis film editing Hollywood cinema point process time series analysis

Metrics
since February 2021
659

Article info
views

315

PDF
downloads


Share


RSS