Exploring the non-Political factors behind young voter enthusiasm: A machine learning approach

Abstract

To build a democratic nation, a voting system provides the foundation and it represents the fundamental rights of citizens to voice their decisions. It also represents the responsibility of citizens for shaping their government. The participation of young voters is important as they form a significant portion of a country and are able to bring new perspectives regarding decision-making or policies revolving around a country. However, a lot of young people refrain from voting due to political violence, thinking it will not make a difference or due to various other reasons. They can be engaged through awareness on social media, political campaigns or discussions in classrooms. Their involvement can increase voter enthusiasm which is pivotal in determining higher voter turnout. With the rise of Artificial Intelligence, the effort of people has decreased significantly in the extraction of data and finding meaningful insights. Hence, we will utilize machine learning to make future decisions based on the primary data we have collected. The aim of this research is to propose a multi-modal agent that can help to infer voter turnout and understand the factors that influence the behavior of our youths when participating in voting using artificial intelligence. We aim to focus only on the non-political factors that affect the voting behavior of young people. We have carried out a survey on the students of BRAC university who were asked to fill out a questionnaire containing both multiple choice questions and opinion based questions. The answers to the multiple choice questions will be processed as tabular data and fed to an artificial neural network for inference. Similarly, the answers to the opinion based questions will be fed to an extreme gradient boosting model for sentiment analysis. The true label for both levels of inference will be whether a person would participate in voting or not. The proposed multi-modal agent will concatenate the outputs from the artificial neural network and the extreme gradient boosting model and provide a final level of prediction. Moreover, to assess the predictions of the artificial neural network, XAI models such as SHAP and LIME will be used to produce global and local level explanations. A further analysis of these explanations will be made to understand which features are affecting our model. Therefore, the proposed model can help us to gauge young voter turnout and shed a light into the influencing factors that steer their decisions.