2004
Volume 4, Issue 1
  • ISSN: 2665-9085
  • E-ISSN: 2665-9085
Preview this article:

There is no abstract available.

Loading

Article metrics loading...

/content/journals/10.5117/CCR2022.1.000.CASA
2022-02-01
2022-09-27
Loading full text...

Full text loading...

/deliver/fulltext/26659085/4/1/CCR2022.1.000.CASA.html?itemId=/content/journals/10.5117/CCR2022.1.000.CASA&mimeType=html&fmt=ahah

References

  1. Baldwin, J., & Schmälzle, R. (2022). A Character Recognition Tool for Automatic Detection of Social Characters in Visual Media Content. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.010.BAL
    [Google Scholar]
  2. Boussalis, C., Coan, T. G., Holman, M. R., & Muller, S. (2021). Gender, Candidate Emotional Expression, and Voter Reactions During Televised Debates. American Political Science Review, First View, 1–16. https://doi.org/10.1017/S0003055421000666
    [Google Scholar]
  3. Chen, K., Kim, S. J., Raschka, S., & Gao, Q. (2022). Visual Framing of Science Conspiracy Videos. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.003.CHEN
    [Google Scholar]
  4. Dietrich, B. J. (2021) Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives. Political Analysis, 29(2), 250–259. https://doi.org/10.1017/pan.2020.25
    [Google Scholar]
  5. Dietrich, B. J., & Ko, H. (2022). Finding Fauci: How Visual and Textual Information Varied on Cable News Networks During the Covid-19 Pandemic. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.004.DIET
    [Google Scholar]
  6. Jürgens, P., Meltzer, C., & Scharkow, M. (2022). Age and Gender Representation on German TV: A Longitudinal Computational Analysis. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.005.JURG
    [Google Scholar]
  7. Lu, Y., & Pan, J. (2022). The Pervasive Presence of Chinese Government Content on Douyin Trending Videos. Computational Communication Research. https://doi.org/10.5117/CCR2022.2.002.LU
    [Google Scholar]
  8. Malik, M. I., Hopp, F. R., & Weber, R. (2022). Representations of Racial Minorities in Popular Movies: A Content-Analytic Synergy of Computer Vision and Network Science. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.006.MALI
    [Google Scholar]
  9. Neumann, M., Franklin Fowler, E., & Ridout, T. N. (2022). Body Language and Gender Stereotypes in Campaign Video. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.007.NEUM
    [Google Scholar]
  10. Peng, Y. (2018). Same Candidates, Different Faces: Uncovering Media Bias in Visual Portrayals of Presidential Candidates with Computer Vision. Journal of Communication, 68(5), 920–941. https://doi.org/10.1093/joc/jqy041
    [Google Scholar]
  11. Peng, Y. (2020). What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision. The International Journal of Press/Politics, 26(1), 143–166. https://doi.org/10.1177/1940161220964769
    [Google Scholar]
  12. Peng, Y. (2022). Athec: A Python Library for Computational Aesthetic Analysis of Visual Media in Social Science Research. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.009.PENG
    [Google Scholar]
  13. Steinert-Threlkeld, Z. C., & Joo, J. (2022). Image as Data: Automated Content Analysis for Visual Presentations of Political Actors and Events. Computational Communication Research. https://doi.org/10.5117/CCR2022.1.001.JOO
    [Google Scholar]
  14. Torres, M., & Cantú, F. (2021). Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data. Political Analysis, First View, 1–19. https://doi.org/10.1017/pan.2021.9
    [Google Scholar]
  15. Webb Williams, N., Casas, A., & Wilkerson, J. D. (2020). Images as Data for Social Science Research: An Introduction to Convolutional Neural Nets for Image Classification. Cambridge University Press.https://doi.org/10.1017/9781108860741
    [Google Scholar]
  16. Won, D., Steinert-Threlkeld, Z. C., & Joo, J. (2017). Protest Activity Detection and Perceived Violence Estimation from Social Media Images. Proceedings of the 25th ACM International Conference on Multimedia. https://doi.org/10.48550/arXiv.1709.06204
    [Google Scholar]
  17. Wu, P. Y., & Mebane, W. R. J. (2022). MARMOT: A Deep Learning Framework for Constructing Multimodal Representations for Vision-and-Language Tasks.Computational Communication Research. https://doi.org/10.5117/CCR2022.1.008.WU
    [Google Scholar]
  18. Xi, N., Ma, D., Liou, M., Steinert-Threlkeld, Z. C., Anastasopoulos, J., & Joo, J. (2020). Understanding the Political Ideology of Legislators from Social Media Images. Proceedings of the International AAAI Conference on Web and Social Media. https://doi.org/10.48550/arXiv.1907.09594
    [Google Scholar]
  19. Zhang, H., & Pan, J. (2019). CASM: A Deep Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media. Sociological Methodology, 49(1), 1–57. https://doi.org/10.1177/0081175019860244
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.5117/CCR2022.1.000.CASA
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error