’15 CityU CCR


The 2015 Workshop on Computational Communication Research (CCR2015)in Chinese Context and Beyond

Organized by

City University of Hong Kong
June 18-20, 2015
Room M5055, Run Run Shaw Creative Media Centre
18 Tat Hong Avenue, Kowloon, Hong Kong
Facebook Group (CCR2015) for Participants

(Click to download the HD version of the group photo)

Content and Format: The CCR2015 Workshop builds on the successful Workshop on Web Mining in 2014, by providing training on major approaches and tools of computational social science for communication research. In particular, the Workshop will focus on the key steps in carrying out computational communication research, including overall design, data collection, data analysis, text mining, and data visualization. All lectures will be a mix between conceptual discussions and hands-on practices (using R language as the primary tool for most sessions). Desktop computers with Internet connection will be provided to all participants. However, participants are strongly advised to bring your own notebook computers with R and RStudio preinstalled.

Workshop Speakers:  The speakers are faculty or PhD graduates of Web Mining Lab, who are social scientists by training with ample experience in developing and applying computational social science tools to communication research questions (often in the context of big data).  They have taught as a team similar workshops at CityU, mainland China, the U.S., and elsewhere.

Intended Participants: The Workshop is designed for academic and applied researchers for communication and media, who are familiar with statistical analysis (e.g., multiple regression, logistic regression, time series analysis, etc.) and R language. Those who have not used R but are familiar with other programming languages (e.g., Python, C, Java, etc.) are advised to learn R in advance, e.g., by following online resources (e.g., R TutorialCode School, Eduraka!, and many others). It will be extremely difficult for anyone without any prior knowledge of statistical analysis and programming to follow the Workshop. A review session on R programming will be provided for those admitted participants who would like to refresh memories of the language. However, the short-review isn’t adequate for anyone to learn R from the scratch.

Application Procedure:

Internal Audience: The Workshop is open, free of charge, for faculty members, doctoral and master’s students, and alumni of Department of Media and Communication, and participants in the 2015 CityU Summer School in Social Science Research, who apply with the attached form by 8 May 2015.  Due to limited seats, later applications will not be entertained.

External Audience: The Workshop will be open for the public from 11 May 2015 if extra seats are available, with further details announced at CCR2015 website.

Workshop Program:

Date/Time Topic Speaker
June 18, 19:00-21:00 Review of R Programming (optional) [Video] Dr. Hai Liang (U of Hong Kong)
June 19, 9:00-12:00 Design and Analysis of CCR [Video] Prof. Jonathan Zhu (City U of Hong Kong)
June 19 14:00-17:00 Web Data Collection and Storage Dr. Hai Liang
June 20 9:00-12:00 Text Data Processing and Mining [Video] Dr. Zhenzhen Wang (City U of Hong Kong)
June 20 14:00-17:00 Data Visualization [Video] Dr. Jie Qin (Savannah College)


Post-workshop Contest: Design a study using CCR methods to address an existing or new question in communication and submit a proposal (about 1000 words) in PDF to CCR2015 on Facebook. The proposal should include the following content:
  • Research question: what exactly will you focus on (e.g., what relationships among what variables) in the study
  • Contributions: why is the study worth doing (e.g., what existing theory/practice to revise or what new theory/practice to create)
  • Procedure: how will you carry out the study (e.g., where and how will you get the data, how will you process, analyze, and visualize the data, etc.)
Reading List
Lecture 0. Review of R Programming
  1. Torfs, P. & Brauer, C. (2014). A (very) short introduction to R. [A brief introduction, http://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf]
  2. Kabacoff. R. I. (2011). R in action: Data analysis and graphics with R. [R for data analysis, http://m.friendfeed-media.com/36d8ab666d485a984e441fd9d0f606c8c8553061]
  1. Anisin, A. (2015). Comparing Python and R for Data Science. [Language comparisons, http://blog.dominodatalab.com/comparing-python-and-r-for-data-science/]
  2. Wickham, W. (2010). ggplot2: Elegant Graphics for Data Analysis (Use R!). [R graphs, http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403]
Lecture 1. Design and Analysis of CCR
  1. Xu, P., Wu, Y., Wei, E., Peng, T. Q., Liu, S., Zhu, J. J., & Qu, H. (2013). Visual analysis of topic competition on social media. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2012-2021. [Time series analysis, http://vis.cse.ust.hk/vislab_homepage/publication/XuWWPLZQ13.pdf]
  2. Peng, T. Q., & Zhu, J. J. (2012). Where you publish matters most: A multilevel analysis of factors affecting citations of internet studies. Journal of the American Society for Information Science and Technology, 63(9), 1789-1803. [Multilevel analysis http://tinyurl.com/where-you-publish-matters-most]
  3. Harris, J. K., Moreland-Russell, S., Tabak, R. G., Ruhr, L. R., & Maier, R. C. (2014). Who says what to whom about #childhoodobesity on Twitter. American Journal of Public Health, 104(7), e62-e69. [Network analysis using ERGM, http://www.ncbi.nlm.nih.gov/pmc/articles/mid/NIHMS597056/pdf/nihms-597056.pdf]
  1. Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, March). Who says what to whom on Twitter. In Proceedings of the 20th International Conference on World Wide Web (pp. 705-714). ACM. [Study of “who”/communicator, http://www.wwwconference.org/proceedings/www2011/proceedings/p705.pdf]
  2. Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. (2009, November). Characterizing user behavior in online social networks. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (pp. 49-62). ACM. [Study of “whom”/audiences, http://koasas.kaist.ac.kr/bitstream/10203/24638/1/imc126-benevenuto.pdf]
  3. Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In Advances in Information Retrieval (pp. 338-349). Springer Berlin Heidelberg. [Study of “what”/content, http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2374&context=sis_research]
  4. Petrovic, S., Osborne, M., McCreadie, R., Macdonald, C., & Ounis, I. (2013). Can Twitter replace newswire for breaking news? Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM2013). [Study of “which channel”/medium, http://homepages.inf.ed.ac.uk/miles/papers/short-breaking.pdf]
  5. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295-298. [A study of “what effects”, http://polisci2.ucsd.edu/rbond/papers/turnout_experiment.pdf]
  6. 祝建华、彭泰权、梁海、王成军、秦洁、陈鹤鑫 (2014). 计算社会科学在新闻传播研究中的应用. 《科研信息化技术与应用》, 5(2), 3-13. [Review in Chinese of CCR studies including the above five, http://escj.cnic.cn/CN/10.11871/j.issn.1674-9480.2014.02.001]
Lecture 2. Web Data Collection and Storage
  1. Hanretty, C. (2013). Scraping the web for arts and humanities, U of East Anglia. [Screen scraping using python, http://www.essex.ac.uk/ldev/documents/going_digital/scraping_book.pdf]
  2. Russell, M. A. (2013). Mining the social web. O’Reilly. [Python for API, Scraping, and so on, http://shop.oreilly.com/product/0636920030195.do]
  3. Fredheim, R. & Zabala, A. (2014) Web scraping using R. [http://quantifyingmemory.blogspot.hk/2014/02/web-scraping-part2-digging-deeper.html]
  1. Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40(1), 129-152. [A review on social science studies using social media data, http://www.annualreviews.org/doi/abs/10.1146/annurev-soc-071913-043145]
  2. Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063. [Pitfalls in social media data, http://www.sciencemag.org/content/346/6213/1063.summary]
Lecture 3. Text Data Processing and Mining
  1. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21, 267-297.
  2. Feinerer, I. (2013). Text mining in R. [R package, http://cran.r-project.org/web/packages/tm/vignettes/tm.pdf ]
  3. Grun, B., & Hornik, K. (2011). Topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1-30. [R package, http://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf ]
  1. Jiang, L. C., Wang, Z. Z., Peng, T. Q., & Zhu, J. J. H. (2015). The divided communities of shared concerns: Mapping the intellectual structure of e-Health research in social science journals. International Journal of Medical Informatics, 84(1), 24-35.
  2. Bisgin, H., Agarwal, N., & Xu, X. (2012). A study of homophily on social media. World Wide Web-Internet and Web Information Systems, 15(213–232).
  3. Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing Twitter and traditional media using topic models. In Advances in Information Retrieval (pp. 338-349). Springer Berlin Heidelberg. [Same as optional reading 3 of lecture 1]
Lecture 4. Data Visualization
  1. HubSpot, & Visage. (2015). Data Visualization 101: How to Design Charts & Graphs. Retrieved from http://cdn2.hubspot.net/hub/53/file-863940581-pdf/Data_Visualization_101_How_to_Design_Charts_and_Graphs.pdf
  2. Machlis, S. (2013). Beginner’s guide to R: Painless data visualization. Retrieved from http://www.computerworld.com/article/2497304/business-intelligence-beginner-s-guide-to-r-painless-data-visualization.html
  3. Roam, D. (2008). The visual thinking codex. In The Back of the Napkin: Solving Problems and Selling Ideas with Pictures. Marshall Cavendish International Asia Pte Ltd. Retrieved from http://www.danroam.com/assets/pdf/tools/TBOTN_codex.pdf
  1. Tufte, E. R. (2001). Aesthetics and technique in data graphical design. In The Visual Display of Quantitative Information (2nd ed., pp. 177–190). Graphics Press. [Available online at http://www.humanities.ufl.edu/pdf/tufte-aesthetics_and_technique.pdf]
  2. Wong, D. M. (2013). The Wall Street Journal guide to information graphics: The dos and don’ts of presenting data, facts, and figures. W. W. Norton, Incorporated. [Preview available at http://zh.scribd.com/doc/14992717/The-Wall-Street-Journal-Guide-to-Information-Graphics]

Contact Information: Dudu Cheng, Department of Media and Communication, City University of Hong Kong, Email ducheng@cityu.edu.hk, phone 34424459, Website: http://weblab.com.cityu.edu.hk/blog/workshops/cityu-ccr

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