Social Media Network and Text Analysis : Online - 3 Day

This course introduces participants to approaches for collecting and analysing network and text data from Social Media, (Twitter, YouTube and Reddit) 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 three days over the following schedule:

 

9.30am -11.00am: Instructional Zoom session (instructor provides demonstration and teaching)
11.00am-11.30am: Break
11.30am-1.00pm: Participants working on set exercises/activities (a Zoom session will allow the instructor to provide 1:1 assistance and also additional instruction to the group)
1.00pm-2.00pm: Lunch
2.00pm-3.30pm: Instructional Zoom session
3.30pm-4.00pm: Break
4.00pm-5.30pm:  Participants working on set exercises/activities

 

 

This course will run on Australian Eastern Daylight Time (UTC +11)

ie Canberra, Sydney Melbourne daylight savings time

 

 
Level 3 - runs over 3 days
Instructor: 
Course dates: Monday 19 February 2024 - Wednesday 21 February 2024
Course status: Course completed (no new applicants)
Venue: 
Online
Week: 
Week 1
About this course: 

This course introduces participants to approaches for collecting and analysing network and text data from social media, (Twitter, YouTube and Reddit) 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, voson.tcn). 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.

 

Course syllabus: 

Day 1

  • Introduction to vosonSML, VOSON Dashboard
  • RStudio and R refresher, including installing R packages
  • SNA using VOSON Dashboard & R/igraph – 1 (network plots, basic node-/network-level metrics)
  • Collecting Twitter data using VOSON Dashboard & vosonSML
  • Text analysis using VOSON Dashboard & R – 1 (text preparation, frequency counts & wordclouds)

 

Day 2

  • Collecting YouTube/Reddit data with VOSON Dashboard & vosonSML
  • SNA using VOSON Dashboard & R/igraph – 2 (clusters, creating subnetworks, coding node attributes - manually and via automated text analysis)
  • Text analysis in R – 2 (sentiment analysis, semantic networks)
  • Collecting WWW hyperlink networks with VOSON Dashboard and vosonSML

 

Day 3

  • Collecting Twitter conversation data using voson.tcn (via Twitter API v2)
  • SNA in R - 3 (Introduction to dynamic network analysis)
  • Text analysis in R - 3 (topic models, introduction to machine learning using text)
  • Introduction to Gephi
  • Introduction to creating reproductible reports in R using knitr and rmarkdown
  • Advanced topics (based on participant interest)

 

Course format: 

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 three days) will be:

9.00am -10.30am: Instructional Zoom session (instructor provides demonstration and teaching)
10.30am-11.00am: Break
11.00am-12.30pm: Participants working on set exercises/activities (a Zoom session will allow the instructor to provide 1:1 assistance and also additional instruction to the group)
12.30pm-1.30pm: Lunch
1.30pm-3.00pm: Instructional Zoom session
3.00pm-3.30pm: Break
3.30pm-5.00pm:  Participants working on set exercises/activities

 

Recommended Background: 

It is advisable that you have taken the following ACSPRI course, or have had some equivalent exposure to social network analysis:

 

It is also advisable that you have some experience with the R programming language (or similar languages) for example, via the following ACSPRI courses:

 

 

Recommended Texts: 

There are no recommended texts, but you can find information on relevant software, (including how to download and install, and help information) here:

 

 

Course fees
Member: 
$2,020
Non Member: 
$3,270
Full time student Member: 
$1,720
FAQ: 

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.

 

Participant feedback: 

 

I have already begun using the new skills acquired from this course in my work

 

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.

 

The instructors actively sought input from us participants and tailored the pacing of the course to our needs throughout all the sessions