Using data science techniques like panel modeling and natural language processing, we’ve uncovered some interesting insights thus far into the 2020 Democratic primary. Feel free to take a look below.
Modeling Political Topics from News Articles and Reddit Comments
During the election cycle, we assume that candidates have much control over the narrative of what they want to discuss, as it relates to their platform and their competitors. Yet, this is not the case. The media heavily influences the topics that are discussed during the election, whether the candidates brought it up or not. Furthermore, we as voters are bringing up topics that are relevant to us on places such as internet forums. What are these topics and how do they contribute to the Hype Machine? We’ve used natural language processing techniques to discover topics mentioned by the media and by voters on the Internet, and how they’ve influenced the primaries thus far.
Media Coverage and the 2020 Democratic Primary
Everybody want’s to know who will win the 2020 Democratic Primary. Our team at Berkeley is here to review some of the storylines so far in the race, and give our impressions on where the race might be headed. This analysis is based on the data described in the Measuring Hype page and the source can be found in our panel models R-markdown file.