text: 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 
  invented spontaneously by users of Twitter in 2007 as a mechanism to categorize discussions [74]. 
  Words with hashtags are recognized as topics and considered worthy of public discussion. Secondly, 
  by attaching # to certain words in tweets, the users intentionally anchor their tweets to certain topics. 
  The operator # explicitly reﬂects the author’s emphasis, which can help us extract rather than infer the 
  author’s identiﬁcation of the topic of the tweets. Our research question is to analyze and visualize 
  the associations of topics in public climate discourse. Compared with other approaches, analyzing 
  hashtags co-occurrence pattern has advantage in extracting the structure of public discussions. 