text: In this research, we were interested in tweets containing either #climatechange or #globalwarming, 
  as these two hashtags exactly correspond to climate change and global warming, respectively, the two 
  competing deﬁnitions of climate issues. We did not follow [79] to include #AGW (anthropogenic global 
  warming) as query hashtags in our research because we think that this refers to global warming in 
  a deﬁned category so cannot be regarded in parallel with the two considered hashtags. We limited the 
  scope of the search to English-language tweets generated between 1 January 2009 and 31 December 
  2018. We only collected tweets containing either of the two hashtags in the body of the tweets rather 
  than those containing these hashtags in the retweeted or quoted text, as we think that retweeted text or 
  quoted texts cannot directly represent the tweeter’s usage pattern of the two terminologies. 
  
  To collect these tweets, we used a Python-based crawler to send requests to the Twitter server to 
  select hashtags, language, start date, and end date as inputs. Once the ﬁrst request was completed, 
  the server responded with a ﬁle in json format and the ﬁrst 20 qualiﬁed tweets in a time-descending 
  order. By parsing the json ﬁle, we obtained a string for the crawler to build the next request and obtain 
  the next 20 tweets. Thus, a loop was written to keep the crawler sending requests and the crawler 
  was automatically terminated when all the qualiﬁed tweets publicly available were collected. Our 
  crawler respected Twitter’s robot.txt and we did not collect, analyze or display any user information in 
  our study. 