Computer Science Department Thesis Defense - Manmeet Kaur Baxi

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Manmeet Kaur Baxi

Thesis title: Quantifying the impact of Twitter activity in political battlegrounds

Abstract: It may be challenging to determine the reach of the information, how well it corresponds with the domain design, and how to utilize it as a communication medium when utilizing social media platforms, notably Twitter, to engage the public in advocating a parliament act, or during a global health emergency. Chapter 3 offers a broad overview of how candidates running in the 2020 US Elections used Twitter as a communication tool to interact with voters. More precisely, it seeks to identify components related to internal collaboration and public participation (in terms of content and stance similarity among the candidates from the same political front and to the official Twitter accounts of their political parties). The 2020 US Presidential and Vice Presidential candidates from the two main political parties, the Republicans and Democrats, are our main subjects. Along with the content similarity, their tweets were assessed for social reach and stance similarity on 22 topics. This study complements previous research on efficiently using social media platforms for election campaigns. Chapter 4 empirically examines the online social associations of the top-10 COVID-19 resilient nations’ leaders and healthcare institutions based on the Bloomberg COVID-19 Resilience Ranking. In order to measure the strength of the online social association in terms of public engagement, sentiment strength, inclusivity and diversity, we used the attributes provided by Twitter Academic Research API, coupled with the tweets of leaders and healthcare organizations from these nations. Understanding how leaders and healthcare organizations may utilize Twitter to establish digital connections with the public during health emergencies is made more accessible by this study. The thesis has proposed methods for efficiently using Twitter in various domains, utilizing the implementations of various Language Models and several data mining and analytics techniques.


Committee Members:
Dr. Vijay Mago (supervisor, committee chair), Dr. Yimin Yang (co-supervisor), Dr. Thiago E Alves de Oliveira, Dr. Gurjit Randhawa (University of Prince Edward Island)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.