# Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks
## 5 Exploratory Data Analysis  
The period of data collection was from 27 May 2018. The collection was initiated a few days before the ECSA conference to allow capturing any conversations leading up to the event. Data collection continued beyond the ECSA conference until 10 October 2018, allowing for a long-term observation of the citizen science community over four months, except for a few technical outages where the data could not be collected. In total, 238,063 tweets were collected, spread over 124 days, collecting an average of around 2000 Tweets per day. It is to be noted that Twitter only releases 1% of their entire Twitter data via their free online streaming service [55]--as a result, the actual number of tweets generated could have been higher. Since the Tweets made available are capped based on the total tweets generated at the time, we see this random nature of the data collection an expected limitation. However, for less popular topics (as were the ones chosen), this limitation was not expected to significantly impact the data being collected [56]. Figure 1 shows the number of Tweets collected throughout the period of data collection.  
As can be seen from the graph, there have been three gaps in the data collection process due to outages. There are two primary peaks--an initial peak immediately before (2 June) the start of the ECSA 2018 conference (3-5 June) and a further peak on the 31 August. Both of these peaks were due to specific tweets that were widely shared by many people in very short amounts of time. Figure 2 presents a tagcloud of hashtags shared on Twitter during the period of data collection.  
Overall, several keywords come across as dominant topics of discussion over the period of time. The top ten shared hashtags are as follows: #citizenscience and #citsci (23401 + 1494), #crowdsourcing and #crowdsource (13534 + 1208), #data (5367), #blockchain (2816), #crowdfunding (2663), #innovation (1959), #cswglobal18 (1705), #ai (1225), #decentralization (1158) and #crypto (1144). A large majority of tweets however have not been associated with hashtags (156,221). Among the tweets without hashtags, the most common keywords (without considering stopwords) were crowdsourcing/crowdsource (56408), data (21242), platform (9930), medical (7831), child (7827). Following the distribution of the most tweeted hashtags, we observe (Figure 3) that there are three major peaks of conversation around the most discussed hashtags--an initial peak around the 3-5 June during the ECSA conference (purple colour, #citizenscience), and two peaks mid-way during the data collection (green colour, #data)Overall, very few tweets (0.04%) have been shared with their geolocations (as seen in Figure 4)--as the map shows, a majority of these tweets have been shared from the US, with a few shared from Europe.  
While this narrative indicates a heavy use of several terms such as crowdsourcing, citizen science, data, blockchain, medical, child and so on, it is important to note the high amount of variation in the topics covered within the citizen science and crowdsourcing community. It is also important to note that a vast majority of the discussions in the space have been retweets (173,568). Retweets are important ways for information to propagate across networks [57] and have been recognised as a key mechanism of information diffusion on Twitter [58]. As such, retweets can potentially indicate the discussions, ideas, articles and news the community engages with and better gauge the ideas the community reads and recognise with [59]. Tweets that were not retweets, on the other hand, can help better understand new content generated by the community.