# Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks
## 7 Understanding Topical Discussions  
Using keywords and hashtags as a means to understand social media messages is helpful while considering a smaller social media dataset with a limited number of tweets that use standardised hashtags and keywords. However, for larger datasets as the one collected and very generic topics such as citizen science and crowdsourcing, it is challenging to employ a manual process. Instead, it was decided to use a topic model approach to automatically interpret the topics of discussions. Topic modelling is an unsupervised learning technique that aims to identify patterns and relationships among text documents [62, 63], and a widely adopted technique used within a variety of domains such as bioinformatics [64], politics [65], transportation [66], etc. For this research, we have used Latent Dirichlet Allocation (LDA) as topic modelling method [67] for understanding topics of discussion. Techniques offer the possibility of scaling up analyses to hundreds of thousands of tweets. The process involved initially taking the entire corpus and cleaning the text to remove unnecessary elements such as stopwords (NLTK's ([[https://www.nltk.org/](https://www.nltk.org/)]([https://www.nltk.org/](https://www.nltk.org/))) English stopwords corpus) and standard punctuations. Furthermore, the process required the removal of specific keywords and hashtags that were too frequent within the corpus to be distributed in most topics--e.g., 'RT', 'rt', '#crowdsourcing', 'crowdsource', '#crowdsourcing', 'crowdsourcing', 'citizenscience' and '#citizenscience'. Upon cleaning the tweets, the tweets were split into words, to be created into a dictionary. The process then created a term frequency inverse document frequency (TFIDF) model based on the corpus, which was further used to create an LDA model based on 7 topics. The number of topics were based on initial manual exploration of the tweets and several experiments to identify a good fit for the number of topics. The final model was then visualised using pyLDAvis which is a Python based implementation of LDA visualisation [68]. Figure 11 presents a visualisation of the distances between the 7 topics identified across the two principle component axes. The barchart on the right indicates the most salient terms across the entire dataset, where saliency indicates the distribution of topics weighted by the terms overall frequency [68]. Clicking on the topic bubbles on the left provides updated visualisations of the terms relevant to the respective topics. While many of the salient terms are fairly generic and common (e.g., alternative, international, fold, voted, etc. ), analysing uncommon terms provides an indication of the different themes that emerge from the analysis. Salient  
Figure 10: Visualising a more zoomed-in view of the inner core showing the strong network connections with several organisations acting as key influencers. terms for each topic were identified and manually queried on the dataset to understand what were the primary discussion themes. Exploration of the topics show the following distribution of the key terms, together with a summary of the themes emerging (terms in italics were considered for manual exploration):  
**Topic 1 keywords:** 'function', _'kavanaugh'_, 'least', _'collins'_, _'brett'_,'signatures', 'possible','supplies', _'susan'_, 'national', 'trying', _'faq'_. Themes: politics (news on crowdsourcing funds for political opponent), promotion of citizen science platform on a national level, frequently asked questions (FAQ). **Topic 2 keywords:** _'alternative'_, _'2025'_, _'fold'_, _'tradition'_, 'uses', _'representative'_,'sc', 'evening', _'newyorkstateq'_, 'watch', 'bird'.