This net of entities and personalities shows who are the main actors nominated in the corpus of news gathered from Google News USA.

Each node is as big as each actor appears in the news’ titles, and is connected to other actors that appear with that in the same title. The weight of the line that connects them is as bold as how many times they appear together.

The actors are tagged with five different categories that change In quantity throughout the course of the three years analyzed.

During the time analyzed, the quantities of these categories change quite significantly: the number of scientific actors decreases through 2015, and they almost disappear in 2016. The number of international actors also decreases through 2015, but what they lose in number of actors is absorbed by the Paris Agreement, one of the most recurrent actors in 2016. The actors from the entertainment area are absorbed by the figure of Leonardo DiCaprio, and in 2016 we see a great rise of Donald Trump, which becomes one of the biggest centers of the net.

Generally, it's possible to see that the conversation around personalities closes around the United States, during the three months analyzed of 2016. Furthermore, we can see that, despite during the time range chosen COP22 was being held, this actor appeared only a few times. The only word strictly connected to the theme is Marrakech, COP22’s location.


With the general query “climate change” on Google News USA, after scraping the first page of news, the main actors that were mentioned in the titles of the articles were gathered. The titles were then analyzed, since they are the primary way the users interface with the news on the web.

With the help of a tool called Aylien, entities and personalities were extracted from the titles themselves. The first result was a list of names, places and entities for each article that the tool was able decipher and link to its Wikipedia page.

With this corpus of entities, each element was tagged, counted and connected, where possible, to other elements that appeared with it. The result is the net of correspondences and mentions of the actors that shape the discussion around climate change in the three years analyzed.


Data source: Google News, Aylien

The dataset used is structured as a network of connections between actors in the three years. The categorization is assigned to the nodes, allowing the creation of the second visualization that shows the actors's dispersion through the years.