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Decoding the Popularity of TV Series: Conclusion, References, and Appendixby@kinetograph
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Decoding the Popularity of TV Series: Conclusion, References, and Appendix

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In this study, researchers attempt to understand the psychology behind TV ratings through character networks, and the different interactions between characters.
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Author:

(1) Melody Yu, Sage Hill School, California, USA.

CONCLUSION

This research aims to investigate the potential relationship between character interactions in an episode of a TV series and the review of that episode. We hypothesize that certain features of the TV series may attract viewers and lead to positive reviews if these features are present in the episodes. To capture some of these attractive features, we use character network analysis to analyze the interactions between characters in three well-known TV series.


To do this, we construct character networks representing the interactions between characters in 74 episode graphs from the three TV shows. We then use network metrics to describe the characteristics of the conversations between characters and apply Spearman’s rank correlation test to identify the metrics that are statistically significant.


The results of this study, shown in Table IV, indicates that there is a statistically significant correlation between character network metrics and TV show reviews. However, the specific network metrics that show significant correlation vary between the three series, with no common significant metric found across all three.


In summary, our research suggests that character networks can have an impact on TV show review scores. By analyzing the interactions between characters in TV series episodes using network metrics, we were able to identify statistically significant correlations between these metrics and review scores. These findings may be useful for TV producers and writers as they consider how to structure their shows and maintain audience engagement. While character networks are not the only factor that determines a show’s success, they do play a role in audience enjoyment and should be carefully considered in the production of future seasons.

REFERENCES

[1] X. Bost, V. Labatut, S. Gueye and G. Linares, “Narrative smoothing: Dynamic conversational network for the analysis of TV series plots,” in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1111-1118.


[2] X. Bost, V. Labatut, S. Gueye and G. Linares, “TV Series - Networks of characters (Version 7),” in figshare. https://doi.org/10.6084/m9.figshare.2199646.v7.


[3] C. -Y. Weng, W. -T. Chu and J. -L. Wu, “Movie Analysis Based on Roles’ Social Network,” in 2007 IEEE International Conference on Multimedia and Expo, 2007, pp. 1403-1406.


[4] D. Elson, N. Dames and K. McKeown “Extracting Social Networks from Literary Fiction,” in ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 11-16, 2010, Uppsala, Sweden. pages 138-147.


[5] A. Agarwal, A. Corvalan, J Jensen and O. Rambow “Social network analysis of alice in wonderland,” In Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature, pages 88–96.


[6] A. Ricardo, J. Miro-Julia, and F. Rossello., “Marvel Universe looks ´ almost like a real social network,” arXiv preprint cond-mat/0202174 (2002).


[7] X. Bost, V. Labatut and G. Linares, “Serial Speakers: a Dataset of TV Series,” in 12th International Conference on Language Resources and Evaluation (LREC 2020),May 2020, Marseille, France p.4256-4264


[8] NetworkX documentation. https://networkx.org

APPENDIX

TABLE VNETWORK METRICS FOR GAME OF THRONES EPISODES.


TABLE VINETWORK METRICS FOR HOUSE OF CARDS EPISODES.


TABLE VIINETWORK METRICS FOR BREAKING BAD EPISODES.


This paper is available on arxiv under CC 4.0 license.