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Human-AI Intelligence 

Multimodal Intelligence | Embodied Cognition | Human-Computer Interaction

Computational Cognitive Science + Human-AI Intelligence

  • Jiang, Y., Dale, R. & Lu, H. (in press). Transformability, generalizability, but limited diffusibility: comparing global vs. task-specific language representations in deep neural networks. Cognitive Systems Research

  • Jiang, Y., & Dale, R. (Working Paper). A Cognitive Science Rosetta Stone for Model Interpretability: Mapping the Learning Curves of Deep Learning Networks

Computational Neuroscience + Representational Learning

  • Miao, G., Jiang, Y., Pluta, A., Dale, R., Steen, F., Lieberman, M. (Working Paper). Shallow or Deep Conversations? A Functional Near-Infrared Spectroscopy (fNIRS): Hyperscanning Study Towards Multimodal Integration

Computational Social Science

Applied Deep Learning | Machine Learning | Social Media

Applied Deep Learning, Computer Vision and Multimodal Neural Network

  • Jiang, Y., (2023). Emotions in Presidential Debates: A Deep-Learning Approach for Detecting Multimodal Affect. Exploring the C-SPAN Archives : Advancing the Research Agenda. In-prepare

  • Jiang, Y. (2023). Automated Nonverbal Cue Detection in Presidential-Debate Videos: An Optimized RNN-LSTM Approach. Communications in Computer and Information Science. Springer, In-prepare​

Applied Machine Learning, Social Media and Social Movements

  • Jiang, Y., Jin, X. & Deng, Q. (2022). Short Video Uprising: How #BlackLivesMatter content on TikTok challenges the protest paradigm. Workshop Proceedings of  the 16th ICWSM Conference on Images in Online Political Communication (PhoMemes). doi: 10.36190/2022.42

  • Shea, C. S., Jiang, Y., & Leung, W. L. (2022). David vs. Goliath: transnational grassroots outreach and empirical evidence from the # HongKongProtests Twitter network. Review of Communication, 22(3), 193-212. 10.1080/15358593.2022.2106793

  • Chen, Y., Shi, Y., Luo, J., Jiang, Y. et al. (2022). How Is Vaping Framed on Online Knowledge Dissemination Platforms?. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022, vol 13558. Springer, Cham. https://doi.org/10.1007/978-3-031-17114-7_7

Computational Tools and Validation

  • Akcakir, G., Jiang, Y., Luo, J., & Noh, S. (2023). Validating a Mixed-Method Approach for Multilingual News Framing Analysis: A case study of COVID-19. Computational Communication Research, 5(2). https://doi.org/10.5117/CCR2023.2.11.AKCA

  • Lai, S., Jiang, Y., Lei, G., Betke, M., Ishwar, P., & Wijaya, D. An Unsupervised Approach to Discover Media Frames. Proceedings of The LREC 2022 workshop on NLP for Political Sciences. par.nsf.gov/biblio/10347514

Ethnographic Fieldwork

  • Chee, W.C., & Jiang, Y. (2023). Understanding the Sociopolitical Participation of Ethnic Minority in Hong Kong: A Cultural Citizenship Study Approach. Ethnic and Racial Studies (SI proposal accepted)

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