This course introduces participants to approaches for collecting and analysing network and text data from Social Media, (Reddit, YouTube and Mastodon) and the WWW (hyperlink networks) using the R statistical software.
This course was formerly known as Big Data Analysis for Social Scientists
The course will be run over four evenings over the following schedule:
- 4.30pm - 7.00pm: Session 1
- 7.00pm - 7.30pm: Break
- 7.30pm - 9.30pm: Session 2
This course is being run on Australian Eastern Daylight Time (UTC +11)
(Canberra, Sydney Melbourne Daylight Savings time)
Prof. Robert Ackland is based in the School of Sociology at the Australian National University (ANU). He was awarded his PhD in economics from the ANU in 2001, and he has been researching online social and organisational networks since 2002. He leads the Virtual Observatory for the Study of Online Networks Lab (http://vosonlab.net) which was established in 2005 and is advancing the social science of the Internet by conducting research, developing research tools, and providing research training. Robert has been teaching masters courses in online research methods and the social science of the internet since 2008 (undergraduate versions of the courses started in 2017). His book Web Social Science: Concepts, Data and Tools for Social Scientists in the Digital Age (SAGE) was published in July 2013. He created the VOSON software for hyperlink network construction and analysis, which was publicly released in 2006. The VOSON R packages for collecting and analysing social media network and text data were released in 2015 (Bryan Gertzel is the lead developer), and to date the packages have been downloaded over 80K times with current downloads of 1K per month.
This course introduces participants to approaches for collecting and analysing network and text data from social media, (Reddit, YouTube and Mastodon) and the WWW (hyperlink networks).
In terms of analysis, the focus is on the application of social network analysis (SNA) and quantitative text analysis to online data.
While the main software used in the course is R, we also introduce Gephi for advanced visualisation. Data collection is via the VOSON R Packages (VOSON Dashboard, vosonSML). We also cover other important R packages for network and text analysis such as: igraph (network analysis and visualisation), quanteda (quantitative analysis of textual data), tidytext (text mining), and wordcloud (text word clouds).
The course will be particularly useful to academics and PhD students who want to become more computationally literate, and those from technical disciplines (e.g. computer science, engineering, information science) who want to become more familiar with social science approaches to analysing social media data. The course will also be useful for people from industry and government whose work involves quantitative analysis of social media data, e.g. for marketing, social research, public relations, brand management, journalism, opinion analysis.
Day 1
- Introduction to vosonSML, VOSON Dashboard
- RStudio and R refresher, including rmarkdown and installing R packages
- SNA using VOSON Dashboard & R/igraph – 1 (network plots, basic node-/network-level metrics)
- Collecting Reddit data using VOSON Dashboard & vosonSML
Day 2
- Text analysis using VOSON Dashboard & R – 1 (text preparation, frequency counts & wordclouds)
- Collecting YouTube data with VOSON Dashboard & vosonSML
- SNA using VOSON Dashboard & R/igraph – 2 (clusters, creating subnetworks, coding node attributes)
Day 3
- Text analysis in R - 2 (sentiment analysis, semantic networks)
- Introduction to Gephi
- Collecting WWW hyperlink networks with VOSON Dashboard and vosonSML
Day 4
- Text analysis in R - 3 (topic models)
- Collecting Mastodon data with vosonSML
- (Optional) Text analysis in R - 4 (introduction to large language models LLMs)
- (Optional) Scraping website text content with rvest
This course will be run online, via Zoom.
To ensure that participants are well prepared for the course, there will be detailed instructions for installing the required software: R, RStudio, required R packages and Gephi. There will also be preliminary exercises (introduction to R and RStudio) that the participants will be expected to complete before the course. The instructor will be available for consultation (via email or Zoom) prior to the course, to provide assistance with installation of software and the preliminary exercises.
The format of the course (all four days) will be:
- 4.30pm - 7.00pm: Session 1
- 7.00pm - 7.30pm: Break
- 7.30pm - 9.30pm: Session 2
It is advisable that you have exposure to social network analysis, for example via the following ACSPRI courses:
It is also advisable that you have some experience with the R programming language (or similar languages) for example, via the following ACSPRI courses:
There are no recommended texts, but you can find information on relevant software, (including how to download and install, and help information) here:
- The R Project for Statistical Computing - http://www.r-project.org
- RStudio - https://rstudio.com/
- vosonSML
- VOSON Dashboard
- voson.tcn (Twitter Conversation Networks)
- Gephi - http://gephi.github.io
Q: Should I have taken an ACSPRI R Course before attempting this course?
A: Not necessarily. However it is advisable that you either have some experience with social network analysis or experience with R (or a similar programming language).
Q: Do I need to have the VOSON Dashboard and vosonSML R packages already installed on my computer?
A: Yes. Instructions and assistance will be provided prior to the commencement of the course, to ensure that these pacjages (and R, Rstudio) are successfully installed.
I have already begun using the new skills acquired from this course in my work
The instructors actively sought input from us participants and tailored the pacing of the course to our needs throughout all the sessions
The presenter's passion for teaching was wonderful. Very responsive
The course is great, the instructor is very knowledgeable and very helpful. It was lucky for me that the course was small as I was able to get help from time to time when I got stuck.