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The hypothesis proposed by [43] suggests that language production may reﬂect lexical association,
and the more often two concepts are simultaneously mentioned in a piece of text, the higher the
chance that the two concepts are associated in the individual’s cognitive map of the world. Therefore,
semantic network analysis, a methodology based on cognitive science, could help researchers infer
the hidden cognitive network structure in the human mind [44] based on the analysis of texts, as the
frequency, semantic distance, and co-occurrence pattern of words can accurately reﬂect their meaning
and links [45]. Several researchers have attempted to build a semantic network of certain climate
events in social media discourse and scientiﬁc publications [46–48]. Research about public perceptions
of global climate change in Qatar conﬁrmed that the observed semantic network of climate concepts is
not random and represents as meaningful clusters [49].
2.3. Hashtags as Frame Vehicles on Social Media
Large amounts of unprompted user-generated data on social media serve as distributed sensors
that better reﬂect public opinion [50] and oﬀer researchers more accurate insight into the associations
of concepts in people’s minds [ 51]. In particular, hashtags, a unique mechanism of social media,
provide researchers with an unprecedented chance to accurately extract the public cognitive framework
contained in texts. To inform or comment [52], social media users attach user-generated tagging with
a hash character “#” to anchor the keywords of their tweet and classify the tweet to a unifying topic,
and other users can view hashtags as beacons to follow speciﬁc topics and join collective discussions.
As a communicative marker, hashtags have been used in a wide range of cases [53] and have contributed
considerably to conversations and social participation [54,55]. In addition to the function as a topical
signiﬁer [56], social scientists noted that hashtags can also represent the context of a tweet [57], ﬂag
an individual’s community membership [58], or indicate shared interests [59]. Several studies adopted
hashtags as frame markers for both content and sentiment analysis [60–65] to eliminate researchers’
subjectivity in frame detection.
In some cases, social media users have long struggled to deﬁne the most appropriate hashtag for
certain events [60,66]. By observing the usage pattern of trending hashtags “#CrimingWhileWhite” and
“#AliveWhileBlack” in a struggle against racism, [67] discovered that two related hashtags generated
in a single event will draw public attention to diﬀerent sides of a story and cause noticeable structural
and linguistic diﬀerences in public discourse.
When hashtags are used in combination with each other, their co-occurrence pattern in big data
can hint to the cognitive associations in people’s thoughts [60,68]. Researchers can extract nonlinear
social media narratives [69] and discover the networked frame crowdsourced by the public [61]. In the
ﬁeld of climate communication, co-occurrence of environment and social-political hashtags is thought
to strengthen the associations between a variety of trans-regional issues in protests and build collective
action frames [ 70]. The role of climate change issues in the Australian federal election was also
discovered with a co-hash analysis [71]. Reference [72] examined how hashtags concerning science,
political campaigns, geographical discussions, societal concerns, and new technologies co-occurred
with “IPCC”, oﬀering a broad context for studying IPCC communication on social media.
There are many other words in tweets besides hashtags to express the author’s intention. Multiple
approaches, such as LDA and STM [32,73], can help to extract topics from unstructured texts. But in
this study, targeting on hashtags is more in line with our research question. Firstly, hashtags were