Robert Ackland
Robert Ackland holds a PhD in economics and is a professor in the School of Sociology at the Australian National University (ANU), specialising in social network analysis, computational social science and the social science of the Internet. Robert leads the Virtual Observatory for the Study of Online Networks (VOSON) Lab (http://vosonlab.net) which he established in 2005 under an ARC Special Research Initiative (e-Research) grant. Robert is a long-term instructor for the Australian Consortium for Social and Political Research Inc. (ACSPRI), and is currently the Chair of ACSPRI.
Sessions
This workshop will introduce participants to open source R packages for online network collection and analysis, developed by the Virtual Observatory for the Study of Online Networks (VOSON) Lab (http://vosonlab.net) at the Australian National University. The workshop will include an introduction to (depending on workshop participant interest and available API access):
- vosonSML (https://github.com/vosonlab/vosonSML) - an R package providing a suite of tools for collecting and constructing networks from social media data. It provides easy-to-use functions for collecting data across popular platforms (Twitter, Reddit, YouTube, WWW hyperlinks, Mastodon) and generating different types of networks for analysis.
- VOSON Dashboard (https://github.com/vosonlab/VOSONDash) - an R/Shiny application providing a graphical user interface for collecting and analysing online networks and associated text data. It builds on a number of R packages, in particular igraph (for network analysis) and vosonSML.
Participants will be shown how to install these packages and their basic operation. Workshop materials will include R scripts, package documentation, notes on analysis of online networks, and examples of research.
Social media platforms such as Twitter/X and Reddit are increasingly important for political communication: opinion leaders and influencers use social media for one-to-many communication, but these spaces also enable ordinary citizens to form opinions by engaging in one-to-one discussion about social and political issues. It is important to understand how social media are facilitating or impeding political deliberation, a process whereby individuals with differing perspectives and opinions engage in discussion, potentially revising their opinions upon hearing the arguments of others.
In this presentation, I outline research into political deliberation on Twitter, conducted as part of the Volkswagen Foundation-funded “Bots Building Bridges (3B)” project. We first used a set of debate- and election-related hashtags to undertake a collection of tweets authored during the first 2020 US presidential debate. We then used the v2 Twitter API to collect the wider Twitter conversations that these tweets were part of, so our final dataset also included debate-related tweets that did not feature the target hashtags. The resulting dataset consists of over 13K reply tree network with a “conversation starter” tweet as the root node and all the subsequent replies and replies-to-replies.
I then present some preliminary findings regarding the deliberative nature of Twitter activity during the first debate, focusing on two types of analysis. First we construct a measure of deliberation involving the depth (proxy for argumentation) and breadth (proxy for representation) of reply tree networks. Second, we construct a random sample of root-to-leaf reply chains extracted from the reply trees, with the chains then manually coded for agreement, conflict, and incivility. An overall aim is to understand how deliberation trajectories vary with topics of discussion and the partisanship of the discussion partners.