Description

This section we attempt to study what users are discussing in the social media about the topic of global warming and climate change, what topics are hotly discussed and where the public interest points, whether user identities are linked to specific topics.

We used the interactive ways to display common topics and specific topics that people pay attention. At the same time you can compare the topics between Weibo and Twitter. We used two colors to separate the twitter and weibo and sorted the quantity of topic from largest to smallest.

The common topics are often caused by international events or news and some powerful people’s words. The specific topics always revolved around the particular problems in the their own country, the news about chinese people eat meat lead to global warming in Weibo and the climate refugee in the twitter. And people focused on the topic about life-style in the weibo and people more cared about political and cultural topics.

Protocol

In the beginning we used two tools to collect data，the Gooseeker for weibo and the TwitterR for twitter.when the data collection is complete, we use regular expressions to sort the data into user information and text information, and then we import that two parts of information into the excel to filter and refine, and finally we combine raw and illustrator to represent the data.

Data

Data source: Weibo, Twitter

Download data

In this part, we calculate the frequency of each keyword in the tweets from the extracted data, in which the statistical difficulty of the Chinese keyword exists in the language, a word may appear in different words, the second point is the process of translation, There will be a loss of semantics. So we try to find the expression of the same meaning of words, although their expression is not one to one correspondence. Finally, we extract only the meaningful words as the theme.