Jungseock Joo
Jungseock Joo
Associate Professor
Office: On Leave 2024-25
Email: jjoo@comm.ucla.edu
Phone: 310-206-3740
Biography
My research is aimed at understanding verbal and non-verbal human communication in mass media and its effects by scalable, computational, and data-driven approaches. A big challenge in modern media studies lies in the sheer amount of data in various forms and multiple modalities. To quantitatively characterize communicative activities in this space, I use automated or semi-automated methods that scale indefinitely. In particular, my research emphasizes the visual dimension of human communication, to understand how we communicate via visual means such as facial expressions or gestures using images or videos. I develop the state-of-the-art computer vision and machine learning techniques to automatically recognize contents from large-scale visual data. I further examine what people intend to mean by visuals and how specific visuals are crafted to achieve various communicative goals. The specific problems that I have worked on include social perception, media bias detection, cultural and behavioral diffusion on social media, and emotion analysis of TV news programs.
Education
Ph.D., Computer Science, UCLA
M.S., Computer Science, Columbia University
B.SE., Computer Science and Engineering, Seoul National University
Research
Artificial intelligence; deep learning; computer vision; human-AI interaction; social and political event analysis; visual persuasion; computational communication
Selected Publications
Automatically Detecting Image-Text Mismatch on Instagram with Deep Learning. Y. Ha, K. Park, S. J. Kim, J. Joo, M. Cha. Journal of Advertising, Accepted.
News and Geolocated Social Media Accurately Measure Protest Size Variation. A. Sobolev, K. Chen, J. Joo, and Z. C. Steinert-Threlkeld. American Political Science Review, 2020.
Communicating Articial Intelligence (AI): Theory, Research, and Practice. S. Nah, J. McNealy, J. Kim, J. Joo. Communication Studies. 2020
Classifying Leadership Domains with Facial Attributes. J. Yoon, J. Joo, E. Park and J. Han. Proceedings of The International Conference on Social Informatics (SocInfo), 2020.
Understanding the Political Ideology of Legislators from Social Media Images. N. Xi, D. Ma, M. Liou, Z. Steinert-Threlkeld, J. Anastasopoulos, and J. Joo. Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2020.
Automated Coding of Televised Leader Displays: Detecting Nonverbal Political Behavior With Computer Vision and Deep Learning. J. Joo, E. P. Bucy, and C. Seidel. International Journal of Communication, 2019.
Characterizing Clickbaits on Instagram. Y. Ha, J. Kim, D. Won, M. Cha, and J. Joo. Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2018.
Social and Political Event Analysis based on Rich Media. J. Joo, ZC Steinert-Threlkeld, J Luo. Proceedings of the 26th ACM international conference on Multimedia, 2018
Toward an Infrastructure for Data-driven Multimodal Communication Research. F. F. Steen, A. Hougaard, J. Joo, I. Olza, C. Cnovas, A. Pleshakova, S. Ray, P. Uhrig, J. Valen-zuela, J. Wony, and M. Turner. Linguistics Vanguard. 2018.
Protest Activity Detection and Violence Estimation from Social Media Images. D. Won, Z. Steinert-Threlkeld, and J. Joo. Proceedings of the 25th ACM International Conference on Multimedia, 2017.
Red Hen Lab: Dataset and Tools for Multimodal Human Communication Research. J. Joo, F. F. Steen, and M. Turner. KI-Knstliche Intelligenz, 2017.
Cultural Diusion and Trends in Facebook Photographs. Q. You, D. Garca-Garca, M. Paluri, J. Luo, and J. Joo. Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2017.
Predicting Popular and Viral Image Cascades in Pinterest. J. Han, D. Choi, J. Joo, and C. Chuah. Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2017.
Fashion Conversation Data on Instagram. Y. Ha, S. Kwon, M. Cha, and J. Joo. Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2017.
Joint Image-Text News Topic Detection and Tracking by Multimodal Topic And-Or Graph. W. Li, J. Joo, H. Qi, S.C. Zhu. IEEE Transactions on Multimedia, 2017.
Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face. J. Joo, F. Steen, S.C. Zhu. Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015.
Hierarchical Organization by And-Or Tree. J. Joo, S. Wang, S.C. Zhu. In J. Wagemans (Ed.), Oxford Handbook of Perceptual Organization, Oxford University Press, 2014
