/Social-Science-with-Unstructured-Data

Social science research cases with unstructured data (mainly image and audio)

Social Science Research with Unstructured Data

Image Data

Research Papers (with DL)

  • Schwemmer, C., Knight, C., Bello-Pardo, E. D., Oklobdzija, S., Schoonvelde, M., & Lockhart, J. W. (2020). Diagnosing Gender Bias in Image Recognition Systems. Socius, 6. https://doi.org/10.1177/2378023120967171

  • 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

    • Description: Transfer learning with VGG16 on photos and LSTM on text to predict the collective actions through social media posts.

  • Guilbeault, D., Nadler, E. O., Chu, M., Sardo, D. R. L., Kar, A. A., & Desikan, B. S. (2020). Color associations in abstract semantic domains. Cognition, 201. https://doi.org/10.1016/j.cognition.2020.104306

  • Boussalis, C., & Coan, T. G. (2020). Facing the electorate: Computational approaches to the study of nonverbal communication and voter impression formation. Political Communication, 1-23. https://doi.org/10.1080/10584609.2020.1784327

  • Boussalis, C., Coan, T., Holman, M., & Müller, S. (2020, November 17). Gender, Candidate Emotional Expression, and Voter Reactions During Televised Debates. https://doi.org/10.31235/osf.io/4kqgr

  • Casas, A., & Williams, N. W. (2019). Images that matter: Online protests and the mobilizing role of pictures. Political Research Quarterly, 72(2), 360-375. https://doi.org/10.1177/1065912918786805

  • Cantú, F. (2019). The fingerprints of fraud: Evidence from mexico’s 1988 presidential election. American Political Science Review, 113(3), 710-726. https://doi.org/10.1017/S0003055419000285

  • Geboers, M. A., & Van De Wiele, C. T. (2020). Machine Vision and Social Media Images: Why Hashtags Matter. Social Media+ Society, 6(2). https://doi.org/10.1177/2056305120928485

  • Garimella, K., & Eckles, D. (2020). Images and Misinformation in Political Groups: Evidence from WhatsApp in India. arXiv preprint https://arxiv.org/abs/2005.09784

  • Neumann, M. (2020). Fair and Balanced? News Media Bias in the Photographic Coverage of the 2016 U.S. Presidential Election. [preprint]

  • Boxell, L. (2018). Slanted images: Measuring nonverbal media bias. [preprint pdf]

  • Joo, J., Bucy, E. P., & Seidel, C. (2019). Automated Coding of Televised Leader Displays: Detecting Nonverbal Political Behavior With Computer Vision and Deep Learning. International Journal of Communication, 13. https://ijoc.org/index.php/ijoc/article/view/10725

  • 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). https://ieeexplore.ieee.org/document/6909429?arnumber=6909429

  • Choi, J., Lee, S. Y., & Ji, S. W. (2020). Engagement in Emotional News on Social Media: Intensity and Type of Emotions. Journalism & Mass Communication Quarterly. https://doi.org/10.1177/1077699020959718

  • Song, J., Han, K., Lee, D., & Kim, S. W. (2018). “Is a picture really worth a thousand words?”: A case study on classifying user attributes on Instagram. PloS one, 13(10). https://doi.org/10.1371/journal.pone.0204938

  • Song, J., Han, K., Lee, D., & Kim, S. W. (2020, March). Understanding emotions in SNS images from posters' perspectives. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 450-457). https://doi.org/10.1145/3341105.3373923

  • Park, H., Song, J., Han, K., & Kim, S. W. (2018, July). I Like Your Tagged Photos, But Do We Know Each Other?: Analyzing the Role of Tags in Like Networks. In 2018 IEEE International Conference on Web Services (ICWS) (pp. 335-338). https://ieeexplore.ieee.org/document/8456372

  • Kim, Y., & Kim, J. H. (2018). Using computer vision techniques on Instagram to link users’ personalities and genders to the features of their photos: An exploratory study. Information Processing & Management, 54(6), 1101-1114. https://doi.org/10.1016/j.ipm.2018.07.005

  • Kim, J. H., & Kim, Y. (2019). Instagram user characteristics and the color of their photos: Colorfulness, color diversity, and color harmony. Information Processing & Management, 56(4), 1494-1505. https://doi.org/10.1016/j.ipm.2018.10.018

  • Kim, Y., Song, D., & Lee, Y. J. (2020). #Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses. International Journal of Environmental Research and Public Health, 17(20), 7550. https://doi.org/10.3390/ijerph17207550

  • Kim, Y., & Kim, J. H. (2020). Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses. Health Informatics Journal. https://doi.org/10.1177/1460458219896673

  • Han, K., Jo, Y., Jeon, Y., Kim, B., Song, J., & Kim, S. W. (2018, July). Photos don't have me, but how do you know me? Analyzing and predicting users on Instagram. In Adjunct publication of the 26th conference on user modeling, adaptation and personalization (pp. 251-256). https://doi.org/10.1145/3213586.3225232

General Review Papers

  • Cantú, F., & Torres, M. (2020). Learning to See: Visual Analysis for Social Science Data. Political Analysis, forthcoming. [preprint pdf]

  • Williams, N. W., 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.

  • Joo, J., & Steinert-Threlkeld, Z. C. (2018). Image as data: Automated visual content analysis for political science. arXiv preprint arXiv:1810.01544. https://arxiv.org/abs/1810.01544

Audio Data

Research Papers

  • Mehr, S. A., Singh, M., Knox, D., Ketter, D. M., Pickens-Jones, D., Atwood, S., ... & Howard, R. M. (2019). Universality and diversity in human song. Science, 366(6468). https://doi.org/10.1126/science.aax0868

  • Knox, D., & Lucas, C. (2019). A dynamic model of speech for the social sciences. Available at SSRN. http://dx.doi.org/10.2139/ssrn.3490753

  • Proksch, S. O., Wratil, C., & Wäckerle, J. (2019). Testing the validity of automatic speech recognition for political text analysis. Political Analysis, 27(3), 339-359. https://doi.org/10.1017/pan.2018.62

  • Rheault, L., & Borwein, S. (2019). Multimodal Techniques for the Study of Affect in Political Videos. preprint. [preprint pdf]