Keywords
Social media analytics, mobile media analytics, financial media analytics, darknet analytics, cryptocurrency, computational advertising, health informatics, analytics of physiological data, 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.
Recent Publications
I. Journal Articles:
Huang, G., & Liang, H. (2021). Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach. Journal of Business Research, 126, 1-11. https://doi.org/10.1016/j.jbusres.2020.12.052
Chen, Z., Jardine, E., & Liu, X. F. & Zhu, J. J. H. (in press). Seeking Anonymity on the Internet: The Knowledge Accumulation Process and Global Usage of the Tor Network. New Media & Society.
Liu, X. F., Ren, H-H., Liu, S-H., & Jiang, X-J. (2021). Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis. EPJ Data Science, 10, [21]. https://doi.org/10.1140/epjds/s13688-021-00276-9
Jiang, X-J., & Liu, X. F. (2021). CryptoKitties Transaction Network Analysis: The Rise and Fall of the First Blockchain Game Mania. Frontiers in Physics, 9, [631665]. https://doi.org/10.3389/fphy.2021.631665
Sharma, K., Zhan, X., Nah, F., Siau, K., and Cheng, M., “Impact of Digital Nudging on Information Security Behavior: An Experimental Study on Framing and Priming in Cybersecurity,” Organizational Cybersecurity Journal: Practice, Process and People, Vol. 1, No. 1, September 2021, pp. 69-91.
Jia, F., Shi, Y., Sia, C., Tan, C.-H., Nah, F., and Siau, K., “Users’ Reception of Product Recommendations: Analyses Based on Eye Tracking Data,” Lecture Notes in Computer Science 12783, F. F.-H. Nah, and K. Siau (editors), Springer, 2021, pp. 90-104.
Wang, X., Song, Y., & Su, Y. (in press). Less fragmented but highly centralized: A bibliometric analysis of research in computational social science. Social Science Computer Review.
Zhao, X. & Wang, X. (in press). Dynamics of networked framing: Automated frame analysis of government media and the public on Weibo with pandemic big data. Journalism & Mass Communication Quarterly.
Bodaghi, A., Oliveira, J., & Zhu, J. J. H. (2021). The fake news graph analyzer: An open-source software for characterizing spreaders in large diffusion graphs. Software Impacts. doi: https://doi.org/10.1016/j.simpa.2021.100182.
Hou, L., Pan, Y. L., & Zhu, J. J. H. (2021). Impact of scientific, economic, geopolitical, and cultural factors on international research collaboration. Journal of Informetrics, 15(3), 101194. doi: 10.1016/j.joi.2021.101194.
Guan, L., Zhang, Y. F., & Zhu, J. J. H. (2021). Predicting information exposure and continuous consumption: Self-level interest similarity, peer-level interest similarity, and global popularity. Online Information Review. doi: 10.1108/OIR-10-2020-0475.
Zhang, Y. F., Wang, L., Zhu, J. J. H., Wang, X. F., & Pentland, A. S. (2021). The strength of structural diversity in online social networks. Research, 2021, 9831621. doi: 10.34133/2021/9831621.
Wang, C. J., & Zhu, J. J. H. (2021). Jumping over the network threshold of information diffusion: Testing the threshold hypothesis of social influence. Internet Research. doi: 10.1108/INTR-08-2019-0313.
Zhang, Y. F., Wang, L., Zhu, J. J. H., & Wang, X. F. (2021). Conspiracy vs science: A large-scale analysis of online discussion cascades. World Wide Web-Internet and Web Information Systems, 24(2), 585-606. doi: 0.1007/s11280-021-00862-x.
Peng, T. Q., Zhou, Y. X., & Zhu, J. J. H. (2020). From filled to empty time intervals: Quantifying online behaviors with digital traces. Communication Methods and Measures, 14(4), 219-238. doi: 10.1080/19312458.2020.1812556.
Zhang, Y. F., Wang, L., Zhu, J. J. H., & Wang, X. F. (2020). Viral vs broadcast: Characterizing the virality and growth of cascades. EPL, 131(2), 28002. doi:10.1209/0295-5075/131/28002.
Peng, T. Q., & Zhu, J. J. H. (2020). Mobile phone use as sequential processes: From discrete behaviors to sessions of behaviors and trajectories of sessions. Journal of Computer-Mediated Communication, 25(2), 129-146. doi:10.1093/jcmc/zmz029.
II. Conference Papers:
Sun, W.-J., Liu, X. F., & Shen, F. (2021) Learning Dynamic User Interactions for Online Forum Commenting Prediction. In 2021 IEEE International Conference on Data Mining (ICDM), December 7-10, 2021, Auckland, New Zealand.
Zhou, Y. X., & Zhu, J. J. H. (2021). The impact of digital media on daily rhythms: Intrapersonal diversification and interpersonal differentiation. In the 71th annual conference of International Communication Association, online, May.
Zhou, Y. X., & Zhu, J. J. H. (2021). The temporal aspects of daily life: Their sequential mediating role in digital media’s effect on well-being. In the 71th annual conference of International Communication Association, online, May.
Zhou, Y. X., & Zhu, J. J. H. (2021). Digital media and the postmodern transformation: The daily rhythm of digital media use across 15 years. In the 71th annual conference of International Communication Association, online, May.
III. Other publications:
張倫、彭泰權、王成軍、梁海、祝建華 (2021).從邊陲到主流的一種自然路徑:華人計算傳播學者的參與和體驗.載於李立峯、黃煜(編),《中華傳播研究的傳承與創新》,香港中文大學出版社,399-419頁。
IV. Grants:
Huang, G. Collaborative Research Fund, the Research Grants Council (Hong Kong), “(Mis)communication, trust, and information environments: A comparative study of the COVID-19 ‘infodemics’ in four Chinese societies,” 2021-2023, Co-Principal Investigator, HKD3,067,301, On-going.
Liu, X. F. The Grand Challenges ICODA COVID-19 Data Science pilot initiative (~USD 70,000). Principal Invesgator.