# Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks
## 6 Network Analysis  
The citizen science and crowdsourcing community were observed forming several smaller topical communities, primarily developing around specific users. The data collected were processed to identify individual links between users using the '@' mentions, where each tweet can have multiple users being mentioned. Individual links were collected and analysed, to be represented as a node-link graph, where each user is represented by a node, and each interaction (mention) is represented by a directed link between users. Figure 5 illustrates an example interaction, where the users @A and @B have mentioned @C in two different tweets. Figure 4: Geographical plot of all the geo-located tweets of the Twitter data, indicating very few tweets have location information. Figure 3: Trendline showing distributions of the top hashtags in the Twitter dataset. #citizenscience—purple, #data—green. With this representation, a directed graph of the entire network was developed and visualised on a standard visualisation and network analysis tool, Gephi. The network was then analysed to estimate global metrics of the final graph to have a quantitative understanding of its structural properties. The network had 37,452 nodes with 57,424 edges. On an average, the average minimum number of steps (avg. path length) between any two nodes in the network was 5.941. The average of the number of edges adjacent to all nodes (degree) in the graph was 2.001, indicating each node had on an average connections with two other nodes. The network had a high modularity value of 0.892, which indicates how well a network can be decomposed into modular components (e.g., hubs, communities, etc.). The average clustering coefficient, which measures on an average, how complete the neighbourhood of a node is, was a low value of 0.130. We also note a high number of strongly connected components (35,263) and weakly connected components (2025). Figure 6a indicates the entire network of 37k users, aligned based on a weighted force-directed layout (using the Force Atlas 2, [60]), set to prevent node overlaps. A force-directed layout is a standard approach for visualising graphs and networks, where users are represented as nodes, and interactions as edges. The primary principle of such layouts is that nodes repel each other, while edges act as attractive forces between two nodes. The force of attraction (\(F_{a}\)) is defined as a linear relation to the distance between two connected nodes \(n1\) and \(n2\) as per  
\[F_{a}(n1,n2)=d(n1,n2)\]  
The force of repulsion is defined by considering the degree values of the two nodes as per  
\[F_{r}(n1,n2)=k_{r}\frac{(deg(n1)+1)(deg(n2)+1)}{d(n1,n2)}\]  
Figure 5: Illustration of a Twitter interaction where nodes indicate Twitter users and links indicate a tweet from a user, tagging another user. Figure 6: (a) Network visualisation of Twitter discussions on citizen science and crowdsourcing; (b) visualisation of the inner core; (c) visualising hubs within the inner core of the network. This is particularly helpful as communities appear as groups of nodes [61]. In the figure, we can note three characteristics: (i) a large number of clusters (red circles), where a central node interacts with a high number of nodes in its vicinity (this, in addition to (ii) explains the high modularity scores); (ii) a heavy core of the network with the largest number of nodes at the heart of the network, with strong connections (this explains the high number of strongly connected components); and (iii) a very large number of users in the periphery of the network with limited interactions with other users (this explains the low average clustering coefficient score of 0.130). The formation of clear clusters is interesting and users in these clusters appear to interact only with central nodes who have strong links with the core of the network or other clusters. Let us consider some examples of such clusters (Figure 7).