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All the hashtags in the tweets were automatically extracted with the Regular Expression Library
in Python. Hashtags were transformed to lowercase letters, and clear synonyms were stemmed
(e.g., #trump, #DonaldTrump, #donaldtrump). As all the tweets in the “climate change” dataset
contained the #climatechange hashtag and all the tweets in the “global warming” dataset contained the
#globalwarming hashtag, we did not document these two hashtags when processing data. The number
of hashtags contained in the two discourses in each year is displayed in Figure 1b. Hashtags whose
frequency was lower than ten times are excluded in the network analysis. As hashtags are intended
to be a topic anchor [52], extremely low frequency means that the hashtag is not recognized socially,
and excluding them helps researchers focus on meaningful rather than occasional associations.
3.3. Measurement
3.3.1. Hashtag Co-Occurrence Network
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.
3.3.2. Temporal Analysis
A temporal analysis was introduced to understand the evolution of the two climate discourses
over a long period. We ﬁrst examined how the two semantic networks evolved in the past years.
All the nodes once ranked top 50 in any of the 10 years were gathered to form a union set for each
dataset. Then, they were clustered according to the strength of their associations in the whole dataset
and mapped with a force-directed layout algorithm in Gephi to produce a graph of nodes. With the
dynamic network function supplied by Gephi, we then added the associations between the nodes
ranked on the top 50 list in 2009 to the graph of nodes and obtained the relationship of the top 50 nodes
for 2009. Similarly, we produced a total of 10 graphs from 2009 to 2018, where the positions of the
nodes on the 10 maps are the same, but the strengths of their associations are diﬀerent to represent the
changes in the associations of key hashtags for each discourse.
The correlation between climate change and global warming discourses was measured every
year to observe whether the two discourses converged or diverged over time. Considering computing
power limitations, only key hashtags ranked in either of the top 50 lists for the two discourses in that
year were included in the calculations. First, we measured to what extent the two discourses resemble
each other in the order of importance for the hashtags in each year. For every year, the top 50 hashtags
in each network were selected with a rank order according to their centrality. Then, Spearman’s rank
correlation coeﬃcient was used to examine the correlation of the rank orders of the selected nodes