Hostname: page-component-77c89778f8-gq7q9 Total loading time: 0 Render date: 2024-07-18T05:57:14.407Z Has data issue: false hasContentIssue false
  • English
  • Français

Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives

Published online by Cambridge University Press:  11 November 2020

Bryce J. Dietrich Open the ORCID record for Bryce J. Dietrich [Opens in a new window]
Bryce J. Dietrich*
Affiliation:
Department of Political Science, University of Iowa, 341 Schaeffer Hall, Iowa City, IA 52242, USA. Email: bryce-dietrich@uiowa.edu
*
Corresponding author Bryce J. Dietrich
Get access Rights & Permissions [Opens in a new window]

Abstract

Although previous scholars have used image data to answer important political science questions, less attention has been paid to video-based measures. In this study, I use motion detection to understand the extent to which members of Congress (MCs) literally cross the aisle, but motion detection can be used to study a wide range of political phenomena, like protests, political speeches, campaign events, or oral arguments. I find not only are Democrats and Republicans less willing to literally cross the aisle, but this behavior is also predictive of future party voting, even when previous party voting is included as a control. However, this is one of the many ways motion detection can be used by social scientists. In this way, the present study is not the end, but the beginning of an important new line of research in which video data is more actively used in social science research.

Keywords

video analysisCongresspolarization
Type
Letter
Information
Political Analysis , Volume 29 , Issue 2 , April 2021 , pp. 250 - 259
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Edited by Jeff Gill

*

The keywords have been corrected. A corrigendum notice detailing this change was also published (DOI: 10.1017/pan.2021.8).

References

Aggarwal, J. K., and Cai, Q.. 1999. “Human Motion Analysis: A Review.” Computer Vision and Image Understanding 73(3):428–440.CrossRefGoogle Scholar
Bogue, A. G., and Marlaire, M. P.. 1975. “Of Mess and Men: The Boarding House and Congressional Voting, 1821–1842.” American Journal of Political Science 45:207–230.Google Scholar
Caldeira, G. A., and Patterson, S. C.. 1987. “Political Friendship in the Legislature.” The Journal of Politics 49(4):953–975.CrossRefGoogle Scholar
Caldeira, G. A., and Patterson, S. C.. 1988. “Contours of Friendship and Respect in the Legislature.” American Politics Quarterly 16(4):466–485.Google Scholar
Casas, A., and Williams, N. W.. 2019. “Images that Matter: Online Protests and the Mobilizing Role of Pictures.” Political Research Quarterly 72(2):360–375.Google Scholar
Cho, W. K. T., and Fowler, J. H.. 2010. “Legislative Success in a Small World: Social Network Analysis and the Dynamics of Congressional Legislation.” The Journal of Politics 72(1):124–135.Google Scholar
Cohen, L., and Malloy, C. J.. 2014. “Friends in High Places.” American Economic Journal: Economic Policy 6(3):63–91.Google Scholar
de Almeida, I. R., Cassol, V. J., Badler, N. I., Musse, S. R., and Jung, C. R.. 2016. “Detection of Global and Local Motion Changes in Human Crowds.” IEEE Transactions on Circuits and Systems for Video Technology 27(3):603–612.CrossRefGoogle Scholar
Dietrich, B. J. 2020. “Replication Data for: Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives.” https://doi.org/10.7910/DVN/YQPEVQ, Harvard Dataverse, V1.CrossRefGoogle Scholar
Dietrich, B. J., Enos, R. D., and Sen, M.. 2019. “Emotional Arousal Predicts Voting on the Us Supreme Court.” Political Analysis 27(2):237–243.CrossRefGoogle Scholar
Dietrich, B. J., Hayes, M., and O’Brien, D. Z.. 2019. “Pitch Perfect: Vocal Pitch and the Emotional Intensity of Congressional Speech.” American Political Science Review 113(4):941–962.CrossRefGoogle Scholar
Gibson, C. 2010. “Restoring Comity to Congress.” Discussion Paper Series 60. Joan Shorenstein Center on the Press, Politics and Public Policy. https://shorensteincenter.org/restoring-comity-to-congress/.Google Scholar
Green, A., and Hogan, B.. 1982. “Gavel to Gavel: A guide to the Televised Proceedings of Congress.” Technical report, Benton Foundation.Google Scholar
Huddy, L., Mason, L., and Aarøe, L.. 2015. “Expressive Partisanship: Campaign Involvement, Political Emotion, and Partisan Identity.” American Political Science Review 109(1):1–17.Google Scholar
Hughes, H. E. et al. 1976. “Toward a Modern Senate: Final Report of the Commission on the Operation of the Senate.” Technical report, U.S. Senate.Google Scholar
Kirkland, J. H. 2011. “The Relational Determinants of Legislative Outcomes: Strong and Weak Ties Between Legislators.” The Journal of Politics 73(3):887–898.CrossRefGoogle Scholar
Koppensteiner, M., and Grammer, K.. 2010. “Motion Patterns in Political Speech and Their Influence on Personality Ratings.” Journal of Research in Personality 44(3):374–379.CrossRefGoogle Scholar
Koprinska, I., and Carrato, S.. 2001. “Temporal Video Segmentation: A Survey.” Signal Processing: Image Communication 16(5):477–500.Google Scholar
Li, T., Chang, H., Wang, M., Ni, B., Hong, R., and Yan, S.. 2014. “Crowded Scene Analysis: A Survey.” IEEE Transactions on Circuits and Systems for Video Technology 25(3):367–386.Google Scholar
Makin, D. A., Willits, D. W., Koslicki, W., Brooks, R., Dietrich, B. J., and Bailey, R. L.. 2019. “Contextual Determinants of Observed Negative Emotional States in Police–Community Interactions.” Criminal Justice and Behavior 46(2):301–318.CrossRefGoogle Scholar
Manchanda, S., and Sharma, S.. 2016. “Analysis of Computer Vision Based Techniques for Motion Detection.” In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), 445–450. IEEE.Google Scholar
Masket, S. E. 2008. “Where You Sit is Where You Stand: The Impact of Seating Proximity on Legislative Cue-Taking.” Quarterly Journal of Political Science 3:301–311.CrossRefGoogle Scholar
Matthews, D. R. 1959. “The Folkways of the United States Senate: Conformity to Group Norms and Legislative Effectiveness.” American Political Science Review 53(4):1064–1089.CrossRefGoogle Scholar
Murthy, G., and Jadon, R.. 2009. “A Review of Vision Based Hand Gestures Recognition.” International Journal of Information Technology and Knowledge Management 2(2):405–410.Google Scholar
Mutz, D. C., and Mondak, J. J.. 2006. “The Workplace as a Context for Cross-Cutting Political Discourse.” The Journal of Politics 68(1):140–155.Google Scholar
Patterson, S. C. 1959. “Patterns of Interpersonal Relations in a State Legislative Group: The Wisconsin Assembly.” Public Opinion Quarterly 23(1):101–109.CrossRefGoogle Scholar
Patterson, S. C. 1972. “Party Opposition in the Legislature: The Ecology of Legislative Institutionalization.” Polity 4(3):344–366.Google Scholar
Paxton, A., and Dale, R.. (2013). “Frame-Differencing Methods for Measuring Bodily Synchrony in Conversation.” Behavior Research Methods 45(2), 329–343.Google ScholarPubMed
Prajapati, D., and Galiyawala, H. J.. 2015. “A Review on Moving Object Detection and Tracking.” International Journal of Computer Application 5(3):168–175.Google Scholar
Ramseyer, F., and Tschacher, W.. 2014. “Nonverbal Synchrony of Head-and Body-Movement in Psychotherapy: Different Signals Have Different Associations With Outcome.” Frontiers in Psychology 5:979.Google ScholarPubMed
Seshadrinathan, K., and Bovik, A. C.. 2007. “A Structural Similarity Metric for Video Based on Motion Models.” In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 1, I-869–I-872. IEEE.Google Scholar
Shafie, A. A., Hafiz, F., and Ali, M. 2009. “Motion Detection Techniques Using Optical Flow.” World Academy of Science, Engineering and Technology 56:559–561.Google Scholar
Torres, M. 2018. “Give me the full picture: Using computer vision to understand visual frames and political communication.” http://qssi.psu.edu/new-faces-papers-2018/torres-computer-vision-and-politicalcommunication.Google Scholar
Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., and Hutchings, V. L.. 2011. “Election Night’s Alright for Fighting: The Role of Emotions in Political Participation.” The Journal of Politics 73(1):156–170.CrossRefGoogle Scholar
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13(4):600–612.CrossRefGoogle ScholarPubMed
Xi, N., Ma, D., Liou, M., Steinert-Threlkeld, Z. C., Anastasopoulos, J., and Joo, J.. 2019. “Understanding the Political Ideology of Legislators From Social Media Images.” Proceedings of the International AAAI Conference on Web and Social Media 14(1):726–737.Google Scholar
Zauner, C. 2010. “Implementation and Benchmarking of Perceptual Image Hash Functions.” Master’s thesis, Upper Austria University of Applied Sciences.Google Scholar
Zhong, Z., Ye, W., Wang, S., Yang, M., and Xu, Y.. 2007. “Crowd Energy and Feature Analysis.” In 2007 IEEE International Conference on Integration Technology, 144–150. IEEE.CrossRefGoogle Scholar
Supplementary material: PDF

Dietrich supplementary material

Dietrich supplementary material

Download Dietrich supplementary material(PDF)
PDF 21.2 MB
Supplementary material: Link

Dietrich Dateset

Dateset

https://doi.org/10.7910/DVN/YQPEVQ
Link