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Panel Discussion

  1. Miao, G. Q., Jiang, Y., Binnquist, A., Pluta, A., Steen, F., Dale, R., & Lieberman, M. (2024). A Deep Neural Network Approach for Integrating Neural and Behavioral Signals: Multimodal Investigation with fNIRS Hyperscanning and Facial Expressions. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 46)

  2. Jiang, Y., Dale, R., & Lu, H. (2023). Transformability, Generalizability, but Limited Diffusibility: Comparing Global vs. Task-Specific Language Representations in Deep Neural Networks. Cognitive Systems Research, 101184.

  3. Jiang, Y. (2023). Automated Nonverbal Cue Detection in Presidential-Debate Videos: An Optimized RNN-LSTM Approach. Communications in Computer and Information

  4. 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 Research5(2).

  5. Chee, W.C., & Jiang, Y. (2023). Understanding the Sociopolitical Participation of Ethnic Minority in Hong Kong: A Cultural Citizenship Study Approach. Sociology of Race and Ethnicity (under review)

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. Jiang, Y., Jin, X. (2022). Using k-Means Clustering to Classify Protest Songs Based on Conceptual and Descriptive Audio Features. In: Rauterberg, M. (eds) Culture and Computing, vol 13324. Springer, Cham.

  11. Xie, L., Li, Z., Ye, X., & Jiang, Y. (2021). Environmental regulation and energy investment structure. Technological Forecasting and Social Change, 167, 120690. doi: 10.1016/j.techfore.2021.120690

  1. Chair. (August, 2022). Misinformation of Politics and Science. 104th AEJMC Annual Conference. Political Communication Division

  2. Panelist. (August, 2022). Best of CT&M Session, Communication Theory and Methodology Division, 104th AEJMC Annual Conference

  3. Panelist. (May, 2021). Protest Mobilization and Collective Action in the Social Media Age. 71st Annual ICA Conference. Political Communication Division

  4. Panel Discussant. (March, 2021). AI Anchor, 5G, and Recommendation Algorithms: Interaction, Adoption, and the Impact of Emerging Communication Technology. Panel Session at AEJMC Midwinter Conference 2021. Communication Technology Division and Graduate Student Interest Group. 

  5. Panelist. (August, 2021). Filter Bubbles and Conspiratorial Thinking. 103rd AEJMC Annual Conference. Political Communication Division

Conference Presentations

  1. Jiang, Y., Lu, H., & Dale, R. (2024). Integrative Understanding of Image and Text in Large Language Vision Models: Evidence from News Image Captions. Conference on Human Factors in Computing Systems (CHI), Workshop on LLMs as Research Tools: Applications and Evaluations (L*ART), Honolulu, Hawaii, USA

  2. Jiang, Y., & Dale, R. (2023). A Cognitive Science Rosetta Stone for Model Interpretability: Mapping the Learning Curves of Deep Learning Networks. Society for Computation in Psychology Annual Conference (SCiP), San Francisco, CA, USA

  3. Jiang, Y., Dale, R. & Lu, H. (2023). Transformability, generalizability, but limited diffusibility: comparing global vs. task-specific language representations in deep neural networks. Society for Computation in Psychology Annual Conference (SCiP), San Francisco, CA, USA

  4. Miao, G., Jiang, Y., Pluta, A., Dale, R., Steen, F., Lieberman, M. (2023). Shallow or Deep Conversations? A Functional Near-Infrared Spectroscopy (fNIRS): Hyperscanning Study Towards Multimodal Integration. Society for Computation in Psychology Annual Conference (SCiP), San Francisco, CA, USA

  5. Jiang, Y., & Dale, R. (2023) Functional Integration of Visual and Auditory Signals in Multimodal Information Processing: A Connectionist-Based Exploration. Oral presented at the 73rd Annual International Communication Association Conference (ICA), Communication Science and Biology Division, Toronto, Canada

  6. Jiang, Y. (2023) Emotions in Presidential Debates: A Deep-Learning Approach for Detecting Multimodal Affect. Oral presented at the 73rd Annual International Communication Association Conference (ICA). Computational Communication Division, Toronto, Canada

  7. Jiang, Y. (Sep, 2022). The Persistence of Political Extremism: An Agent-Based Explanation. 104th AEJMC Annual Conference. Political Communication Division, Detroit, MI, First Place Student Paper**

  8. Jiang, Y., Lai, S., Lei, G., Betke, M., Ishwar, P., & Wijaya, D. (August, 2022). Community Detection of the Framing Element Network: Proposing and Assessing a New Computational Framing Analysis Approach. 104th AEJMC Annual Conference. Communication Theory and Methodology Division, Detroit, MI, Top Method Paper **

  9. Jiang, Y. (Sep, 2021). Effects of ideology and participation on electoral conspiracy endorsement. 2021 APSA  Annual Meeting. Ideas and Knowledge as Causal Variables Division, Seattle, WA, USA

  10. Jiang, Y., Jin, X. & Deng, Q. (Sep, 2021). Legitimizing Protests via Multimedia Platforms: Evidence from TikTok in #BLM. 2021 APSA Annual Meeting. Political Communication Division, Seattle, WA, USA

  11. Jiang, Y. (Aug, 2021). Conspiracy Mentality, Motivated Reasoning, Conspiracy Adoption: Effects of Ideology and Participation on Electoral Conspiracy Endorsement. Submitted to the 103rd AEJMC Annual Conference. Political Communication Division, New Orleans, LA, USA. Third Place Student paper*

  12. Shea, C., Jiang, Y., & Leung, W. (Aug, 2021). Asking the enemy of my enemy for help: Transnational grassroots outreach on Twitter in the #HongKongProtests. Submitted to the 103rd AEJMC Annual Conference. Political Communication Division, New Orleans, LA, USA

  13. Jiang, Y. (Aug, 2021). Understanding Triggers of Problematic Internet Uses in Casual Mobile Game Designs. Submitted to the 103rd AEJMC Annual Conference. Graduate Student Interest Group, New Orleans, LA, USA. Master's Award Paper*

  14. Jin, X., An, Z., & Jiang, Y. (2021) Effects of Hong Kong Local Identity on the Intention to Use Health Code during COVID-19. Oral presented at the 2021 Association for Education in Journalism and Mass Communication Annual Conference (AEJMC). New Orleans, United States

  15. Jiang, Y., Jin, X., & Deng, Q. (2021) TikTok Matters: How Short-form Video Platforms Challenge the Protest Paradigm in the #BlackLivesMatter Movement. Oral presented at the 71st Annual International Communication Association Conference (ICA). Political Communication Division, Denver, United States

  16. Jiang, Y., (Aug, 2020). Psychological Factors of Fandoms Engagement in East Asian 'Pop Idol Group' Culture: The Impact of Self-identity Construction and Social Capital Acquisition. Scholar-to-scholar paper session at the 103th AEJMC Annual Conference. Entertainment Studies Interest Group, San Fransisco, CA, USA 

  17. Jiang, Y., (Apr, 2019). Understanding fandoms in East Asian “pop idol group’"culture. Poster presentation at the Western Psychology Association 99th Annual Conference, Pasadena, CA, USA

White Paper & Visualization

This was exploratory and experimental research to find out which topics stood out in Ugandan media coverage of Covid-19 according to a machine learning method called LDA Topic Modelling, which employs a Natural Language Processing (NLP) technique.

The research project “Communicating COVID-19” examines international news coverage of the ongoing spread of Coronavirus disease 2019 (COVID-19). We use a combination of communication research methods and data science tools to identify main topics emphasized in the news coverage of different languages and update our results weekly.

We currently have results based on the news coverage in several countries and regions including Mainland China, Hong Kong, Taiwan, Egypt, Germany, South Korea, the United States, the United Kingdom, and Uganda. I am in charge of data collection, data analysis, and topic modeling of Uganda

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