text: The co-occurrence patterns of hashtags in tweets from two datasets were documented to build 
  semantic networks for climate change and global warming. For instance, for “#cimatechange 
  redistributes #ﬁsh species at high latitudes. @_OScience @AarhusUni #Arctic”, a tweet in the climate 
  change dataset, hashtags #ﬁsh and #arctic were documented as co-occurring and their associations plus 
  one in the semantic network of climate change. In the semantic network, nodes represent hashtags and 
  the weight of edge refers to the frequency at which two hashtags co-occurred. 
  
  We visualized the network using Gephi software [81]. Following the established literature 
  [60,61,82], only the most prominent hashtags were included in the visualization to concentrate our 
  analysis on the most important hashtags. In this research, the top 50 hashtags with the highest centrality 
  in each network were selected for visualization. Modularity analysis was then analyzed to identify the 
  clusters of hashtags in each semantic network, and hashtags belonging to the same cluster were drawn 
  in the same color. The network spatialization was conducted with Gephi’s built-in force-directed 
  layout algorithm proposed by Fruchterman and Reingold [83], where the more associated the hashtags, 
  the closer they are to each other in the spatial layout. 