Cheng-Jun Wang is currently an associate professor in the School of Journalism and Communication, Nanjing University. He is the director of Computational Communication Collaboratory and the Socrates Lab. He is also a research member of Web Mining Lab, City University of Hong Kong. His research interest focuses on employing big data, computational methods, and social theories to study human communication behaviors, including but not limited to information diffusion, attention flow, and computational narrative. His research on computational communication appears in both SSCI and SCI indexed journals, such as Internet Research, Cyberpsychology, Telematics and Informatics, Scientific Reports, PloS ONE, and Physica A. You can find his CV here.
Ph.D in Communication, 2014
City University of Hong Kong
MA in Communication, 2010
BS in Journalism, 2008
Feb 6th, 2022, I am recruiting well motivated and dedicated
Ph.D. and master students. Undergraduate students are also encouraged to contact me by my email: firstname.lastname@example.org. The application materials should include you CV, transcript, and personal statement.
Please kindly notethat the url of the online book has been changed to https://chengjun.github.io/mybook/.
I am teaching the following courses:
My ebook Elements of Computational Communication (In Chinese 《计算传播学网络讲义》）is now available online, enjoy! https://chengjun.github.io/mybook.
I find the Essay Writing Guide written by Jordan B Peterson is very useful. I convert it into slides and add some annotations. The Power of Writing: Ten Steps of Essay Writing. In addition, I highly recommend Howard Becker’s book Writing for Social Scientists (Notebook & Slides).
I am organizing the Tycho Reading Club to discuss the computational social science every Wendesday at News Coffee. You can learn about our future activies by scanning the QR code below or watch the videos of prior activites.
We know that people often desire something but do not really want it. Don’t be afraid to really want what you desire. – Slavoj Žižek 🚀