# Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks
## 6 Network Analysis
The two clusters have the central user @Turing_Net (TuringNet AI Platform, Figure 7a) and @eventum_network (Eventum Network, discussing cryptocurrency news, Figure 7a). Both these users can be observed to have in-bound interactions with large number of users. Upon analysing the tweets, it is observed that these interactions are mainly retweets of initial tweets posted by these users. Turing_Net (496 conversations) for example, had tweeted (on 29 August 2018):  
```
''One company won't do. #Decentralization of #AI is the way to go! #TuringNet will crowdsource the improvement of AI, foster a healthy ecosystem where all participants will receive a fair share of rewards. @APompliano @ETCN@crypto @AINewsletter @techreview @ForbesTech @techradar [[https://t.co/NukJ9](https://t.co/NukJ9) iEdv1]([https://t.co/NukJ9](https://t.co/NukJ9) iEdv1)''
```  
which was favourited 469 times. A response to this tweet (on 15 September 2018),  
```
''Great product, hopt to see turning being use in the future @kurulpier @Turing_Net @kmlevo @BerbertRSim @6 ShillionPeople @CryptboomNews @YudhaPu88192935 @fris7_Jura [[https://t.co/rlzllyIKB4](https://t.co/rlzllyIKB4)]([https://t.co/rlzllyIKB4](https://t.co/rlzllyIKB4))''
```  
was retweeted (55) and favourited (115) multiple times during the period of data collection, which explains the cluster developing around Turing_Net. A similar interaction was observed in the @eventum_network cluster, where large numbers of users retweeted the following tweet:  
```
''RT@eventum_network: Read more about the Ozgame betting experiment and how Eventum helped power it #Blockchain @crowdsourcing... ''
```  
In fact, examining several such clusters (red circles) in Figure 5(a) shows the majority of the interactions are in-bound, and primarily retweets of popular users/tweets. The following illustrates the different activities:  
* **Crowdsourcing/funding initiatives to:** find stolen laptop; support a defamation case against a politician; seek stories on sharing/hiring/borrowing assets or persons; seek lawyers willing to help the Philippine Long Distance Telephone Company (PLDT) employees for their contract termination;  
Figure 7: Examples of small clusters of users—(**a**) Turing Net AI Platform and (**b**) Eventum Network  
* **Promotion of:** a hackathon to crowdsource mobility solutions; a homebuyer's app; a marine mammal surveyor course on citizen science; maternity t-shirts; a non-profit organisation to promote Democratic candidates;
* **News/Opinions regarding:** how crowdsourcing (WhatsApp) was used to disrupt anti-terrorist operations in Kashmir; the release of a book on crowdsourcing for filmmakers; US DOJ's actions on election integrity. Observing a variety of such clusters (groups) indicates the primary function of them as support clusters, based on specific topics and purposes. As observed in Figure 6a, the large number of clusters surround a heavy core at the centre. This core (Figure 6b), consisting of around 23.8 k nodes and 41.5 k edges was isolated from the wider network, and studied for different characteristics (Figure 8). Figure 8 shows the inner core network visualised based on a force-directed layout from a variety of perspectives. Figure 8a visually encodes the nodes based on their closeness centrality, which indicates the average distance from a node to all other nodes in the network. As can be seen, the cluster of the nodes on the bottom right appears to have the highest values. These nodes are primarily connected (53 k retweets) to the network via only @dataeum, a collaborative and decentralised platform for data generation, based on blockchain and crowdsourcing. Much of the information shared via these nodes are hence related to cryptocurrency, decentralization, blockchain and crowdsourcing. These interactions help explain the relative distance of these nodes to the others in the network. Figure 8b visualises the same network, nodes colour-coded based on weighted in-degree values.