Department of Media and Communication Center for Communication Research

Computational Communication Research


Social media analytics, mobile media analytics, financial media analytics, darknet analytics, cryptocurrency, computational advertising, health informatics, etc.

Research Interests

Computational communication research employs computational methods such as user log analytics, text mining, online experiment, network analysis, and etc. to address both long-standing and emerging questions that bear either theoretical or applicational importance in media and communication. As the name suggests, it is an interdisciplinary approach across communication, computer science, data science, management science, physical science, engineering, and many neighboring social sciences. The following is a partial list of ongoing projects undertaken by members of the clusters with funding support from GRF, PPR, ITC, NSSFC, and other sources.

  • Interplay among media coverage, public opinion, and government policy during the COVID19 outbreak. The project aims to track and evaluate how the media, the public, and the government around the world interact with each other about and react to COVID19. The findings are expected to help prepare the society worldwide to cope with major public health crises in the future.
  • Transmission of misinformation during the COVID19 outbreak. The project aims to track and evaluate how misinformation spreads across social media in times of the pandemic outbreak. The findings are expected to enhance understanding of misinformation dissemination and help organizations and communicators to find ways to counteract misinformation during the public health crises.
  • Knowledge graph of social media research. The project aims to construct a knowledge graph of conceptual ontology and empirical findings of social media research, based on the latest techniques of natural language processing and deep learning. The resulting knowledge graph is expected to serve scholarly syntheses and practical applications of empirical social media studies.
  • Social media analytics for financial market surveillance. The project aims to use AI-driven technology and social media big data to develop and deploy a set of intelligence systems to monitor financial environment in Hong Kong and beyond. The resulting systems is expected to help financial industry, government regulators, and the general consumers to keep abreast of the rapid changing financial ecosystems.
  • Darknet and cryptocurrency analytics. The project centers around the mining of behavior patterns of darknet and cryptocurrency users. The findings are expected to help fight against drug abuse, Internet crimes and financial frauds.

Recent Publications

Peng, T. Q., & Zhu, J. J. H. (in press). Mobile phone use as sequential processes: From discrete behaviors to sessions of behaviors and trajectories of sessions. Journal of Computer-Mediated Communication.

Zhao, W. X., Hou, Y. P., Chen, J. H., Zhu, J. J. H., Yin, E. J., Su, H. T., & Wen, J. R. (2020). Learning semantic representations from directed social links to tag microblog users at scale. ACM Transactions on Information Systems, 38(2), 17. doi: 10.1145/3377550.

Huang, G., & Liang, H. (2020, March). I (dis)trust what you wrote: Uncovering the effects of textual features in information diagnosticity and adoption of online consumer reviews. Paper presented at the annual conference of the American Academy of Advertising, San Diego, CA, USA.

Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., Gonzalez-Bailon, S., Lamberso, P. J., Pan, J., Peng, T. Q., Shen, C. C., Smaldino, P. E., Van Atteveldt, W., Waldherr, A., Zhang, J., & Zhu, J. J. H. (2019). Computational Communication Science| Computational Communication Science: A Methodological Catalyzer for a Maturing Discipline. International Journal of Communication, 13, 3912–3934.

Guan, L., Peng, T. Q., & Zhu, J. J. H. (2019). Who is tracking health on mobile devices: A behavioral logfile analysis in Hong Kong. Journal of Medical Internet Research. 7(5), 313679. doi: 10.2196/13679.

Peng, T. Q., Liang, H., & Zhu, J. J. H. (2019). Introducing computational methods for computational communication research in Asia-Pacific. Asian Journal of Communication, 29(3), 205-216. doi: 10.1080/01292986.2019.1602911.

Zhu, J. J. H., Guan, L., Zhou, Y. X., Shen, A. Q., & Lu, H. (2019). Applying user analytics to social media in China. Asian Journal of Communication, 29(3),291-306. doi: 10.1080/01292986.2019.1602916.

Wang, C. J., & Zhu, J. J. H. (2019). Jumping onto the bandwagon of collective gatekeepers: Testing the bandwagon effect of information diffusion on social news website. Telematics and Informatics, 41(8), 34-45. doi: 10.1016/j.tele.2019.03.001.

Zhan, X. X., Liu, C., Zhou, G., Zhang, Z. K., Sun, G. Q., Zhu, J. J. H., & Jin, Z. (2018). Coupling dynamics of epidemic spreading and information diffusion on complex networks. Applied Mathematics and Computation, 332, 437-448. doi: 10.1016/j.amc.2018.03.050.

Zhu, J. J. H., Chen, H. X., Peng, T. Q., Liu, X. F., & Dai, H. X. (2018). How to measure sessions of mobile phone use: Quantification, evaluation, and applications. Mobile Media and Communication, 6(2), 215-232. doi: 10.1177/2050157917748351.