Master Thesis Defense: Ellison Yin Nang Chan

Speaker: Ellison Yin Nang Chan

Supervisors: Drs. A. Krzyzak and C. Y. Suen

Examining Committee: Drs. T. Glatard, C. Poullis, T.-H. Chen (Chair)

Title: Predicting US Elections with Social Media and Neural Networks

Date: Friday, March 29, 2019

Time: 15:00

Place: EV 2.260

ABSTRACT

Increasingly, politicians and political parties are engaging their electors using social media. In the US Federal Election of 2016, candidates from both parties made heavy use of Social Media, particularly Twitter. It is then reasonable to attempt to find a correlation between popularity on Twitter, and eventual popular vote in the election. In this thesis, we will focus on using the subscriber ‘location’ field in the profile of each candidate to estimate support in each state.

A major challenge is that the Twitter location field in a user profile is not constrained, requiring the application of machine learning techniques to cluster users according to state.

In this thesis, we will train a Deep Convolutional Neural Network (CNN) to classify place names by state. Then we will apply the model to the Twitter Subscriber ‘location’ field of Twitter subscribers collected from each of the two candidates, Hillary Clinton (D), and Donald Trump (R). Finally, we will compare predicted popular votes in each state, to the actual results from the 2016 Presidential Election.

The hypothesis is that a city name has a strong correlation to the people who founded it and then incorporated it. Further, it’s hypothesized that the original settlers were mostly homogeneous, relative to the country of origin and shared a common language, thus resulting in place names using the language of their origin.

In addition to learning the pattern related to the State Names, this additional information may help a machine learning model learn to classify locations by state.