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|pdfUrl=https://ceur-ws.org/Vol-2540/FAIR2019_paper_12.pdf
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https://ceur-ws.org/Vol-2540/FAIR2019_paper_12.pdf
Graph analysis of Twitter feed network maps
The detection of network patterns within the South
African Twitter community
Stefanie Strachan1 and Aurona Gerber1,2[0000-0003-1743-8167]
1 Department of Informatics, University of Pretoria, Pretoria, South Africa
2 Centre for AI Research (CAIR), South Africa
Keywords: Social Network Analysis, Graph Analysis, Twitter Networks, Com-
munity Clusters, Network Visualisation
Extended Abstract. In the past decade various studies were done on the usage and
influence of the internet, technology, and social media in society [1, 2, 3]. These studies
indicate that social media has become a popular means of communication and therefore
forms a significant part of what determines the views and opinions of society [4]. It is
therefore increasingly important to analyse and understand social media networks and
information flows. Understanding the ways that online communities form and com-
municate, as well as the different underlying network structures, could possibly assist
with identifying key influencers as well as the formation and dynamics of social net-
works and communities [5]. The information can be utilised, for example, in the design
of necessary interventions, or the identification of targeted campaigns [6].
The aim of this study was to extend previous research [7] and investigate the poten-
tial network structures that are formed within the South African Twitter community
around the 2019 elections by means of knowledge graphs with an existing toolset
namely NodeXL. Previous research and findings suggest that social network maps of
Twitter communities can be analysed and visualised to provide insight into the setting
of social media. The network maps point out previously unknown information about
the persons and topics that drive conversations and group behaviour [8]. The main re-
search question answered by this study can be stated as follows: Are there any distinct
network patterns that can be detected within the South African Twitter community, spe-
cifically concerning recent political concerns?
Twitter datasets on the SA elections during three specific periods were collected,
namely, the last week that political campaigning was allowed (28 April 2019 – 4 May
2019), Election day on 8 May 2019, and Inauguration day on 24 May 2019. The datasets
were imported into NodeXL, and unique entities, or vertices, were determined. The
vertices were grouped into clusters using the Clauset-Newman-Moore algorithm that
analyses how the vertices are connected to one another. A range of measures of the
graph was calculated for the overall network, as well as for each vertex in the network.
Each of the network metrics summarizes a different aspect of the size and shape of the
overall network and the location and connection properties of each entity in the network
graph. For all three datasets groups with less than ten vertex connections as well as
neighbourless edges were removed from the graph in order to focus the network
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2
analysis on the core network. Subgraph images were created for each vertex before the
overall network was visualized using the NodeXL visualisation feature. Figure 1 below
depicts the graph of Dataset 3 – Inauguration Day (23-24 May 2019).
Figure 1: Network visualisation for Dataset 3
All three of the datasets that were analysed depicted community clusters that have a
large number of disconnected entities that contribute to the network by mentioning the
topic but does not link to other entities within the network. Both the main, larger groups
as well as the smaller, sub-groups are highly interconnected and only share a small
percentage of connections across groups. Community clusters often reveal diversity of
opinion on a specific topic that exists within a society. Since South Africa has multiple
political parties, multiple groups are formed, both on the general subject of the elec-
tions, as well as centred around specific political groups or influencers. In Datasets 1&2
groups shared a common topic but had a different focus, while the groups in Dataset 3
remain segregated.
In conclusion Twitter has become a popular means of communication and conver-
sations create networks with identifiable groups and connections as users reply to and
mention one another. Mapping social networks can assist in understanding the different
ways that individuals form communities and organize online. Previous research identi-
fied six distinct patterns in the social structures depending on the topic in question [9].
The results of this study on South African Twitter feeds around the 2019 elections iden-
tified the community clusters pattern, which means a large number of disconnected
entities contribute to the network by mentioning the topic but does not link to other
entities within the network. Both the main, larger groups as well as the smaller or sub-
groups are highly interconnected and only shared a small percentage of connections
across groups. The most central or influential users for each network could also be de-
termined.
3
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