2004
Volume 4 Number 1
  • E-ISSN: 2665-9085

Abstract

Abstract

Images matter because they help individuals evaluate policies, primarily through emotional resonance, and can help researchers from a variety of fields measure otherwise difficult to estimate quantities. The lack of scalable analytic methods, however, has prevented researchers from incorporating large scale image data in studies. This article offers an in-depth overview of automated methods for image analysis and explains their usage and implementation. It elaborates on how these methods and results can be validated and interpreted and discusses ethical concerns. Two examples then highlight approaches to systematically understanding visual presentations of political actors and events from large scale image datasets collected from social media. The first study examines gender and party differences in the self-presentation of the U.S. politicians through their Facebook photographs, using an off-the-shelf computer vision model, Google’s Label Detection API. The second study develops image classifiers based on convolutional neural networks to detect custom labels from images of protesters shared on Twitter to understand how protests are framed on social media. These analyses demonstrate advantages of computer vision and deep learning as a novel analytic tool that can expand the scope and size of traditional visual analysis to thousands of features and millions of images. The paper also provides comprehensive technical details and practices to help guide political communication scholars and practitioners.

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2022-02-01
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References

  1. Ahler, D. J., Citrin, J., Dougal, M. C., & Lenz, G. S. (2017). Face Value? Experimental Evidence that Candidate Appearance Influences Electoral Choice. Political Behavior, 39 (1), 77–102. http://doi:10.1007/s11109-016-9348-6
    [Google Scholar]
  2. Al-Rawi, A. K. (2015, mar). Sectarianism and the Arab Spring: Framing the popular protests in Bahrain. Global Media and Communication, 11 (1), 25–42. Retrieved from http://gmc.sagepub.com/cgi/doi/10.1177/1742766515573550http://doi:10.1177/1742766515573550
    [Google Scholar]
  3. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., & Parikh, D. (2015). Vqa: Visual question answering. In International conference on computer vision (pp. 2425–2433).
    [Google Scholar]
  4. Atkinson, M. D., Enos, R. D., & Hill, S. J. (2009). Candidate Faces and Election Outcomes: Is the Face–Vote Correlation Caused by Candidate Selection?Quarterly Journal of Political Science, 4, 229–249. http://doi:10.1561/100.00008062
    [Google Scholar]
  5. Barrett, A. W., & Barrington, L. W. (2005). Is a picture worth a thousand words? newspaper photographs and voter evaluations of political candidates. Harvard International Journal of Press/Politics, 10 (4), 98–113.
    [Google Scholar]
  6. Barry, A. M. S. (1997). Visual Intelligence: Perception, Image, and Manipulation in Visual Communication. SUNY Press.
    [Google Scholar]
  7. Bauer, N. M., & Carpinella, C. (2018). Visual information and candidate evaluations: the influence of feminine and masculine images on support for female candidates. Political Research Quarterly, 71 (2), 395–407.
    [Google Scholar]
  8. Baum, M. A., & Groeling, T. (2008). New Media and the Polarization of American Political Discourse. Political Communication, 25 (4), 345–365. http://doi:10.1080/10584600802426965
    [Google Scholar]
  9. Baumeister, R. F., Bratslavsky, E., & Vohs, K. D. (2001). Bad Is Stronger Than Good. Review of General Psychology, 5 (4), 323–370. http://doi:10.1037//1089-2680.5.4.323
    [Google Scholar]
  10. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19 (7), 711–720.
    [Google Scholar]
  11. Benford, R. D., & Snow, D. A. (2000). Framing Processes and Social Movements: An Overview and Assessment. Annual Review of Sociology, 26, 611–639.
    [Google Scholar]
  12. Bennett, W. L., & Segerberg, A. (2013). The Logic of Connective Action (No. June2013). Cambridge: Cambridge University Press.
    [Google Scholar]
  13. Biggs, M. (2016). Size Matters: Quantifying Protest by Counting Participants. Sociological Methods & Research, 1–33. http://doi:10.1177/0049124116629166
    [Google Scholar]
  14. Blumler, J. G., & Kavanagh, D. (1999). The Third Age of Political Communication: >Influences and Features. Political Communication, 16 (3), 209–230. http://doi:10.1080/105846099198596
    [Google Scholar]
  15. Bonica, A. (2018). Inferring Roll-Call Scores from Campaign Contributions Using Supervised Machine Learning. American Journal of Political Science, 62 (4), 830–848. http://doi:10.1111/ajps.12376
    [Google Scholar]
  16. Brader, T., Valentino, N. A., & Suhay, E. (2008). What Triggers Public Opposition to Immigration? Anxiety, Group Cues, and Immigration Threat. American Journal of Political Science, 52 (4), 959–978. http://doi:10.1111/j.1540-5907.2008.00353.x
    [Google Scholar]
  17. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77–91).
    [Google Scholar]
  18. Cantu, F. (2019). The Fingerprints of Fraud: Evidence From Mexico’s 1988 Presidential Election. American Political Science Review, 113 (3), 710–726. http://doi:10.13140/RG.2.2.34763.49442
    [Google Scholar]
  19. Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018). Vggface2: A dataset for recognising faces across pose and age. In 2018 13th ieee international conference on automatic face & gesture recognition (fg 2018) (pp. 67–74).
    [Google Scholar]
  20. Carpinella, C. M., Hehman, E., Freeman, J. B., & Johnson, K. L. (2016). The Gendered Face of Partisan Politics: Consequences of Facial Sex Typicality for Vote Choice. Political Communication, 33 (1), 21–38. http://doi:10.1080/10584609.2014.958260
    [Google Scholar]
  21. Carter, E. B., & Carter, B. L. (2019). Propaganda and Protest in Autocracies. Journal of Conflict Resolution.
    [Google Scholar]
  22. Casas, A., & Webb Williams, N. (2019). Images That Matter: Online Protests and the Mobilizing Role of Pictures. Political Research Quarterly, 72 (2), 360–375. http://doi:10.2139/ssrn.2832805
    [Google Scholar]
  23. Chen, D., Park, K., & Joo, J. (2020). Understanding gender stereotypes and electoral success from visual self-presentations of politicians in social media. In Joint workshop on aesthetic and technical quality assessment of multimedia and media analytics for societal trends (pp. 21–25).
    [Google Scholar]
  24. D’Alessio, D., & Allen, M. (2000). Media bias in presidential elections: A meta-analysis. Journal of communication, 50 (4), 133–156.
    [Google Scholar]
  25. Delalleau, O., & Bengio, Y. (2011). Shallow vs. deep sum-product networks. In Advances in neural information processing systems (pp. 666–674).
    [Google Scholar]
  26. Dietrich, B. J. (2018). Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives. Working Paper. Retrieved from http://www.brycejdietrich.com/files/working{_}papers/Dietrich{_}cspan.pdf
    [Google Scholar]
  27. Druckman, J. N., & Parkin, M. (2005). The impact of media bias: How editorial slant affects voters. The Journal of Politics, 67 (4), 1030–1049.
    [Google Scholar]
  28. Eldan, R., & Shamir, O. (2016). The power of depth for feedforward neural networks. In Conference on learning theory (pp. 907–940).
    [Google Scholar]
  29. Enli, G. (2017). Twitter as arena for the authentic outsider: exploring the social media campaigns of trump and clinton in the 2016 us presidential election. European journal of communication, 32 (1), 50–61.
    [Google Scholar]
  30. Feinberga, M., Willer, R., & Kovacheff, C. (2017). Extreme Protest Tactics Reduce Popular Support for Social Movements. Working paper, 1–58. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract{_}id=2911177http://doi:10.1007/s10551-015-2769-z.For
    [Google Scholar]
  31. Feldman, S., & Johnston, C. (2014). Understanding the determinants of political ideology: Implications of structural complexity. Political Psychology, 35 (3), 337–358.
    [Google Scholar]
  32. Fenno, R. F. (1978). Home style: House members in their districts.Pearson College Division.
    [Google Scholar]
  33. Fleming, M. K., & Cottrell, G. W. (1990). Categorization of faces using unsupervised feature extraction. In Ijcnn (pp. 65–70).
    [Google Scholar]
  34. Gazzaniga, M. S. (1998). The Mind’s Past. University of California Press.
    [Google Scholar]
  35. Geise, S., & Baden, C. (2015). Putting the image back into the frame: Modeling the linkage between visual communication and frame-processing theory. Communication Theory, 25 (1), 46–69.
    [Google Scholar]
  36. Gentzkow, M., & Shapiro, J. M. (2010). What drives media slant? Evidence from U.S. Daily Newspapers. Econometrica, 78 (1), 35–71. http://doi:10.3982/ECTA7195
    [Google Scholar]
  37. Gibson, R., & Zillmann, D. (2000). Reading between the photographs: The influence of incidental pictorial information on issue perception. Journalism & Mass Communication Quarterly, 77 (2), 355–366.
    [Google Scholar]
  38. Gilliam Jr, F. D., & Iyengar, S. (2000). Prime suspects: The influence of local television news on the viewing public. American Journal of Political Science, 560–573.
    [Google Scholar]
  39. Gitlin, T. (1980). The Whole World Is Watching: Mass Media in the Making and Unmaking of the New Left, With a New Preface. University of California Press.
    [Google Scholar]
  40. Grabe, M. E., & Bucy, E. P. (2009). Image bite politics: News and the visual framing of elections. Oxford University Press.
    [Google Scholar]
  41. Graber, D. A. (1996). Say It with Pictures. The ANNALS of the American Academy of Political and Social Science, 546 (1), 85–96. http://doi:10.1080/08858190209528804
    [Google Scholar]
  42. Griffiths, T. L., Jordan, M. I., Tenenbaum, J. B., & Blei, D. M. (2004). Hierarchical topic models and the nested chinese restaurant process. In Advances in neural information processing systems (pp. 17–24).
    [Google Scholar]
  43. Grimmer, J. (2010). A bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Analysis, 18 (1), 1–35.
    [Google Scholar]
  44. Groeling, T., Joo, J., Li, W., & Steen, F. (2016). Visualizing presidential elections. In Annual meeting of the american political science association.
    [Google Scholar]
  45. Guidotti, R., Monreale, A., & Ruggieri, S. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51 (5), 93:1–93:42.
    [Google Scholar]
  46. Haim, M., & Jungblut, M. (2020). Politicians’ self-depiction and their news portrayal: Evidence from 28 countries using visual computational analysis. Political Communication, 1–20.
    [Google Scholar]
  47. Hamdy, N., & Gomaa, E. H. (2012, apr). Framing the Egyptian Uprising in Arabic Language Newspapers and Social Media. Journal of Communication, 62 (2), 195–211. http://doi:10.1111/j.1460-2466.2012.01637.x
    [Google Scholar]
  48. Hansen, L. (2015). How images make world politics: International icons and the case of abu ghraib. Review of International Studies, 41 (2), 263–288.
    [Google Scholar]
  49. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778).
    [Google Scholar]
  50. Hellmeier, S., Weidmann, N. B., & Geelmuyden Rød, E. (2018). In The Spotlight:Analyzing Sequential Attention Effects in Protest Reporting. Political Communication, 00 (00), 1–25. http://doi:10.1080/10584609.2018.1452811
    [Google Scholar]
  51. Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016). Generating visual explanations. In European conference on computer vision (pp. 3–19).
    [Google Scholar]
  52. Hsiang, S. M., Burke, M., & Miguel, E. (2013, sep). Quantifying the influence of climate on human conflict. Science, 341 (6151), 1235367. http://doi:10.1126/science.1235367
    [Google Scholar]
  53. Jagenstedt, P. (2008). How much a thousand words are worth. Retrieved 2018-04-15, from https://blog.foolip.org/2008/05/17/how-much-a-thousand-words-are-worth/
    [Google Scholar]
  54. Johns, R., & Shephard, M. (2007). Gender, Candidate Image and Electoral Preference. British Journal of Politics and International Relations, 9 (3), 434–460. http://doi:10.1111/j.1467-856X.2006.00263.x
    [Google Scholar]
  55. Joo, J., Bucy, E., & Seidel, C. (2019). Automated coding of televised leader displays: Detecting nonverbal political behavior with computer vision and deep learning. International Journal of Communication, 13, 4044-4066.
    [Google Scholar]
  56. Joo, J., Steen, F. F., & Zhu, S.-C. (2015). Automated facial trait judgment and election outcome prediction: Social dimensions of face. In International conference on computer vision (pp. 3712–3720).
    [Google Scholar]
  57. Joo, J., Li, W., Steen, F. F., & Zhu, S. C. (2014). Visual persuasion: Inferring communicative intents of images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 216–223).
    [Google Scholar]
  58. Jost, J. T., Federico, C. M., & Napier, J. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual review of psychology, 60, 307–337.
    [Google Scholar]
  59. Kanade, T. (1977, January). Computer recognition of human faces. Interdisciplinary Systems Research, 47, 1–47.
    [Google Scholar]
  60. Kang, Z., Indudhara, C., Mahorker, K., Bucy, E. P., & Joo, J. (2020, August). Understanding political communication styles in televised debates via body movements. In European Conference on Computer Vision (pp. 788–793). Springer, Cham.
    [Google Scholar]
  61. Kargar, S., & Rauchfleisch, A. (2019). State-aligned trolling in Iran and the double-edged affordances of Instagram. New Media & Society, 1–22. http://doi:10.1177/1461444818825133
    [Google Scholar]
  62. Kärkkäinen, K., & Joo, J. (2019). Fairface: Face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913.
    [Google Scholar]
  63. Ketelaars, P., Walgrave, S., & Wouters, R. (2017). Protesters on message? Explaining demonstrators’ differential degrees of frame alignment. Social Movement Studies, 16 (3), 340–354. Retrieved from http://dx.doi.org/10.1080/14742837.2017.1280387http://doi:10.1080/14742837.2017.1280387
    [Google Scholar]
  64. Kiros, R., Salakhutdinov, R., & Zemel, R. (2014). Multimodal neural language models.In International conference on machine learning (pp. 595–603).
    [Google Scholar]
  65. Kraidy, U. (2002). Digital Media and Education: cognitive impact of information visualization. Journal of Educational Media, 27 (3), 95–106. http://doi:10.1080/1358165022000081369
    [Google Scholar]
  66. Kreiss, D., Lawrence, R. G., & McGregor, S. C. (2019). Political identity-ownership: Symbolic contests to represent members of the public. Working Paper.
    [Google Scholar]
  67. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
    [Google Scholar]
  68. Lam, O., Wojcik, S., Broderick, B., & Hughes, A. (2018). Gender and Jobs in Online Image Searches (Tech. Rep.). Pew Research Center.
    [Google Scholar]
  69. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521 (7553), 436–444.
    [Google Scholar]
  70. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1 (4), 541–551.
    [Google Scholar]
  71. Lenz, G. S., & Lawsom, C. (2011). Looking the Part: Television Leads Less Informed Citizens to Vote Based on Candidates’ Appearance. American Journal of Political Science, 55 (3), 574–589. http://doi:10.1111/j.1540-5907.2011.00511.x
    [Google Scholar]
  72. Lim, M. (2013, mar). Framing Bouazizi: ’White lies’, hybrid network, and collective/connective action in the 2010-11 Tunisian uprising.Journalism, 14 (7), 921–941. http://doi:10.1177/1464884913478359
    [Google Scholar]
  73. Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In International conference on computer vision (pp. 3730–3738).
    [Google Scholar]
  74. Livingston, S., & Bennett, W. L. (2003). Gatekeeping, Indexing, and Live-Event News: Is Technology Altering the Construction of News? Gatekeeping , Indexing , and Live-Eve. Political Communication, 20 (4), 363–380. http://doi:10.1080/10584600390244121
    [Google Scholar]
  75. Marcus, G. E., Neuman, W. R., & MacKuen, M. (2000). Affective intelligence and political judgment. Chicago: University of Chicago Press.
    [Google Scholar]
  76. Mattes, K., & Milazzo, C. (2014). Pretty faces, marginal races: Predicting election outcomes using trait assessments of British parliamentary candidates. Electoral Studies, 34, 177–189. http://doi:10.1016/j.electstud.2013.11.004
    [Google Scholar]
  77. McGregor, S. C., Lawrence, R. G., & Cardona, A. (2017). Personalization, gender, and social media: Gubernatorial candidates’ social media strategies. Information, communication & society, 20 (2), 264–283.
    [Google Scholar]
  78. Moore, W. H. (1995, jun). Rational Rebels: Overcoming the Free-Rider Problem. Political Research Quarterly, 48 (2), 417–454. http://doi:10.1177/106591299504800211
    [Google Scholar]
  79. Myers, D. J., & Caniglia, B. S. (2004). All the Rioting That’s Fit to Print: Selection Effects in National Newspaper Coverage of Civil Disorders, 1968-1969. American Sociological Review, 69, 519–543.
    [Google Scholar]
  80. Nulty, P., Theocharis, Y., Popa, S. A., Parnet, O., & Benoit, K. (2016). Social media and political communication in the 2014 elections to the european parliament. Electoral studies, 44, 429–444.
    [Google Scholar]
  81. 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.
    [Google Scholar]
  82. Popkin, S. L. (1994). The Reasoning Voter: Communiation and Persuasion in Presidential Campaigns. Chicago: University of Chicago Press.
    [Google Scholar]
  83. Purdy, C. (2018). China is launching a dystopian program to monitor citizens in Beijing. Retrieved 12.20.2018, from https://qz.com/1473966/china-is-starting-a-big-brother-monitoring-program-in-beijing/
    [Google Scholar]
  84. Ranjan, R., Patel, V. M., & Chellappa, R. (2017). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (1), 121–135.
    [Google Scholar]
  85. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 779–788).
    [Google Scholar]
  86. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91–99).
    [Google Scholar]
  87. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58 (4), 1064–1082.
    [Google Scholar]
  88. Rojas, M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic prediction of facial trait judgments: Appearance vs. structural models. PloS one, 6 (8), e23323.
    [Google Scholar]
  89. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … others (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115 (3), 211–252.
    [Google Scholar]
  90. Schill, D. (2012). The visual image and the political image: A review of visual communication research in the field of political communication. Review of Communication, 12 (2), 118–142.
    [Google Scholar]
  91. Schmuck, D., & Matthes, J. (2017). Effects of Economic and Symbolic Threat Appeals in Right-Wing Populist Advertising on Anti-Immigrant Attitudes: The Impact of Textual and Visual Appeals. Political Communication, 34 (4), 607–626. http://doi:10.1080/10584609.2017.1316807
    [Google Scholar]
  92. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Computer vision and pattern recognition (pp. 618–626).
    [Google Scholar]
  93. Shaban, H. (2018, jun). Amazon employees demand company cut ties with ICE. Retrieved from https://www.washingtonpost.com/news/the-switch/wp/2018/06/22/amazon-employees-demand-company-cut-ties-with-ice
    [Google Scholar]
  94. Shah, D. V., Hanna, A., Bucy, E. P., Lassen, D. S., Van Thomme, J., Bialik, K., … Pevehouse, J. C. (2016). Dual screening during presidential debates: Political nonverbals and the volume and valence of online expression. American Behavioral Scientist, 60 (14), 1816–1843.
    [Google Scholar]
  95. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
    [Google Scholar]
  96. Snow, D. A., Rochford Jr., E. B., Worden, S. K., & Benford, R. D. (1986). Frame Alignment Processes, Micromobilization, and Movement Participation. American Sociological Review, 51 (4), 464–481.
    [Google Scholar]
  97. Soroka, S., Loewen, P., Fournier, P., & Rubenson, D. (2016). The impact of news photos on support for military action. Political Communication, 33 (4), 563–582.
    [Google Scholar]
  98. Steinert-Threlkeld, Z. C. (2018). Twitter as Data. Cambridge University Press.
    [Google Scholar]
  99. Steinert-Threlkeld, Z. C.,Chan, A., & JooJ. Forthcoming. “How State and Protest Violence Affect Protest Dynamics”. DOI: 10.1086/715600. Available at: https://www.journals.uchicago.edu/doi/pdf/10.1086/715600
    [Google Scholar]
  100. Stephan, M. J., & Chenoweth, E. (2008). Why Civil Resistance Works. International Security, 33 (1), 7–7.
    [Google Scholar]
  101. Stier, S., Bleier, A., Lietz, H., & Strohmaier, M. (2018). Election campaigning on social media: Politicians, audiences, and the mediation of political communication on facebook and twitter. Political communication, 35 (1), 50–74.
    [Google Scholar]
  102. Stovall, J. G. (1988). Coverage of 1984 presidential campaign. Journalism Quarterly, 65 (2), 443–449.
    [Google Scholar]
  103. Sullivan, D. G., & Masters, R. D. (1988). “happy warriors”: Leaders’ facial displays, viewers’ emotions, and political support. American Journal of Political Science, 345–368.
    [Google Scholar]
  104. Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Computer vision and pattern recognition (pp. 1701–1708).
    [Google Scholar]
  105. Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of competence from faces predict election outcomes. Science, 308 (5728), 1623–1626.
    [Google Scholar]
  106. Torres, M. (2018). Give me the full picture: Using computer vision to understand visual frames and political communication. Retrieved from http://qssi.psu.edu/new-faces-papers-2018/
    [Google Scholar]
  107. Towner, T. L., & Muñoz, C. L. (2018). Picture perfect? the role of instagram in issue agenda setting during the 2016 presidential primary campaign. Social science computer review, 36 (4), 484–499.
    [Google Scholar]
  108. Tukachinsky, R., Mastro, D., & King, A. (2011). Is a Picture Worth a Thousand Words? The Effect of Race-Related Visual and Verbal Exemplars on Attitudes and Support for Social Policies. Mass Communication and Society, 14 (6), 720–742. http://doi:10.1080/15205436.2010.530385
    [Google Scholar]
  109. Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2011). Election forecasts with twitter: How 140 characters reflect the political landscape. Social science computer review, 29 (4), 402–418.
    [Google Scholar]
  110. Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104 (2), 154–171.
    [Google Scholar]
  111. Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., & Hutchings, V. L. (2011). Election Night’s Alright for Fighting: The Role of Emotions in Political Participation. Journal of Politics, 73 (1), 156–170. http://doi:10.1017/S0022381610000939
    [Google Scholar]
  112. Vernon, R. J., Sutherland, C. A., Young, A. W., & Hartley, T. (2014). Modeling first impressions from highly variable facial images. Proceedings of the National Academy of Sciences, 111 (32), E3353–E3361.
    [Google Scholar]
  113. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57 (2), 137–154.
    [Google Scholar]
  114. Watts, M. D., Domke, D., Shah, D. V., & Fan, D. P. (1999). Elite cues and media bias in presidential campaigns: Explaining public perceptions of a liberal press. Communication Research, 26 (2), 144–175.
    [Google Scholar]
  115. Wittebols, J. H. (1996). News from the Noninstitutional world: U.S. and Canadian television news coverage of social protest. Political Communication1, 13 (3), 345–361. http://doi:10.1080/10584609.1996.9963122
    [Google Scholar]
  116. Won, D., Steinert-Threlkeld, Z. C., & Joo, J. (2017). Protest activity detection and perceived violence estimation from social media images. In Proceedings of the 2017 acm on multimedia conference (pp. 786–794).
    [Google Scholar]
  117. Wouters, R., & Walgrave, S. (2017). Demonstrating Power: How Protest Persuades Political Representatives. American Sociological Review, 82 (2), 361–383. http://doi:10.1177/0003122417690325
    [Google Scholar]
  118. 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. In Proceedings of the international aaai conference on web and social media (Vol. 14, pp. 726–737).
    [Google Scholar]
  119. You, Q., Cao, L., Cong, Y., Zhang, X., & Luo, J. (2015). A multifaceted approach to social multimedia-based prediction of elections. IEEE Transactions on Multimedia, 17 (12), 2271–2280.
    [Google Scholar]
  120. Zebrowitz, L. A., & Montepare, J. M. (2008). Social psychological face perception: Why appearance matters. Social and personality psychology compass, 2 (3), 1497–1517.
    [Google Scholar]
  121. Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818–833).
    [Google Scholar]
  122. 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, 1–48.
    [Google Scholar]
  123. Zhang, Q.-s., & Zhu, S.-C. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electrical Engineering, 19 (1), 27–39.
    [Google Scholar]
  124. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, 40 (6), 1452–1464.
    [Google Scholar]
  125. Bengio, Yoshua, PatriceSimard and PaoloFrasconi. 1994. “Learning long-term dependencies with gradient descent is difficult.”IEEE transactions on neural networks5(2):157–166.
    [Google Scholar]
  126. Collier, Paul and AnkeHoeffler. 2004. “Greed and grievance in civil war.”Oxford Economic Papers56:563–595.
    [Google Scholar]
  127. Eldan, Ronen and OhadShamir. 2016. The power of depth for feedforward neural networks. In Conference on Learning Theory. pp. 907–940.
    [Google Scholar]
  128. Gebru, Timnit, JonathanKrause, YilunWang, DuyunChen, JiaDeng, ErezLieberman Aiden and LiFei-Fei. 2017. “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States.”Proceedings of the National Academy of Sciences114(50):13108–13113.
    [Google Scholar]
  129. Glaeser, Edward L, ScottDuke Kominers, MichaelLuca and NikhilNaik. 2018. “Big data and big cities: The promises and limitations of improved measures of urban life.”Economic Inquiry56(1):114–137.
    [Google Scholar]
  130. He, Kaiming, XiangyuZhang, ShaoqingRen and JianSun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778.
    [Google Scholar]
  131. Hsiang, Solomon M, MarshallBurke and EdwardMiguel. 2013. “Quantifying the influence of climate on human conflict.”Science341(>6151), 1235367.
    [Google Scholar]
  132. Hunziker, Philipp, CarlMüller-Crepon and Lars-ErikCederman. 2018. “Roads to Rule, Roads to Rebel: Relational State Capacity and Conflict in Africa.”. URL:http://roads-to-peace.org/PDF/DIIS%20UNOPS%202017%20Roads%20to%20Peace%20report.pdf
    [Google Scholar]
  133. Hunziker, Philipp and Lars-ErikCederman. 2017. “No Extraction without Representation: The Ethno-Regional Oil Curse and Secessionist Conflict.”Journal of Peace Research54(3):365–381.
    [Google Scholar]
  134. Jensen, Jr and DcCowen. 1999. “Remote sensing of urban suburban infrastructure and socio-economic attributes.”Photogrammetric Engineering and Remote Sensing65(5):611–622.
    [Google Scholar]
  135. Kern, Holger Lutz. 2011. “Foreign Media and Protest Diffusion in Authoritarian Regimes: The Case of the 1989 East German Revolution.”Comparative Political Studies44(9):1179–1205.
    [Google Scholar]
  136. Krizhevsky, Alex, IlyaSutskever and GeoffreyE Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. pp. 1097–1105.
    [Google Scholar]
  137. LeCun, Yann, BernhardBoser, JohnS Denker, DonnieHenderson, RichardE Howard, WayneHubbard and LawrenceD Jackel. 1989. “Backpropagation applied to handwritten zip code recognition.”Neural computation1(4):541–551.
    [Google Scholar]
  138. Odgers, Candice L., AvshalomCaspi, ChristopherJ. Bates, RobertJ. Sampson and TerrieE. Moffitt. 2012. “Systematic social observation of children’s neighborhoods using Google Street View: a reliable and cost-effective method.”The Journal of Child Psychology and Psychiatry53(10):1009–1017.
    [Google Scholar]
  139. Poggio, Tomaso, HrushikeshMhaskar, LorenzoRosasco, BrandoMiranda and QianliLiao. 2017. “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review.”International Journal of Automation and Computing14(5):503–519.
    [Google Scholar]
  140. Premise Data. 2017. “Premise Data.”. URL:www.premise.com
  141. Selvaraju, Ramprasaath R, MichaelCogswell, AbhishekDas, RamakrishnaVedantam, DeviParikh and DhruvBatra. 2017. Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization. In Computer Vision and Pattern Recognition. pp. 618–626.
    [Google Scholar]
  142. Tapiador, Francisco J., SilvaniaAvelar, CarlosTavares-Correa and RainerZah. 2011. “Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery.”International Journal of Remote Sensing32(21):6437–6456.
    [Google Scholar]
  143. Toté, Carolien, DomingosPatricio, HendrikBoogaard, Raymondvan der Wijngaart, ElenaTarnavsky and ChrisFunk. 2015. “Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique.”Remote Sensing7(2):1758–1776.
    [Google Scholar]
  144. Weidmann, Nils B and SebastianSchutte. 2017. “Using night light emissions for the prediction of local wealth.”Journal of Peace Research54(2):125–140.
    [Google Scholar]
  145. Wilson, Jeffrey S, CherylM Kelly, MarioSchootman, ElizabethA Baker, AniruddhaBanerjee, MorganClennin and DouglasK Miller. 2012. “Assessing the built environment using omnidirectional imagery.”American journal of preventive medicine42(2):193–199.
    [Google Scholar]
  146. Won, Donghyeon, ZacharyC Steinert-Threlkeld and JungseockJoo. 2017. Protest Activity Detection and Perceived Violence Estimation from Social Media Images. In Proceedings of the 2017 ACM on Multimedia Conference. ACM pp. 786–794.
    [Google Scholar]
  147. ZacharyC.Steinert-Threlkeld, Alexander Chan, and JungseockJoo. Forthcoming. “How State and Protest Violence Affect Protest Dynamics”. DOI: 10.1086/715600. Available at: https://www.journals.uchicago.edu/doi/pdf/10.1086/715600
    [Google Scholar]
  148. Zhang, Han and JenniferPan. 2019. “CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media.”Sociological Methodology49:1–48.
    [Google Scholar]
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