Characterizing Social Media Platforms and their Users in Response to Real-Life Events

PhD Thesis Proposal Defence


Title: "Characterizing Social Media Platforms and their Users in Response 
to Real-Life Events"

by

Mr. Ehsan Ul HAQ


Abstract:

User-generated content within online social networks, discussion forums, 
and news media provides a wide-ranging mirror of society and has become a 
vital data source for researchers. With the constant emergence of novel 
platforms and the ever-changing nature of communication modalities, user 
participation and discussion are continuously evolving. Each new 
generation of users is more likely to adapt to the newer platforms rather 
than join older platforms, requiring new observations and insights. User 
demographics and location play a particular role in platform adaptation 
and usage; however, offline events such as elections, political 
discussions or crises lead to an extensive discourse on such platforms. 
Such events trigger an inertial moment that brings people into a 
large-scale and multi-faceted online discourse; such discourse can result 
in a polarized community or a collective effort for social activism. These 
processes eventually form an opinion among the masses and may generate 
societal bias towards specific issues.

There is extensive literature on social media usage and bias mitigation on 
user-generated content across different demographics. However, there is 
less focus on the non-trivial and more dynamic characterization of users 
and content. For instance, there is limited literature on niche social 
media platforms like Instagram (popular for lifestyle photography) for 
social activism or more specific online communities such as WallStreetBets 
(famous for stock trading) on Reddit concerning offline events. In 
addition to the characterization of platforms and their users in dynamic 
settings such as for social activism, a critical question is how we can 
account for human bias while studying this highly subjective 
user-generated content. Human bias and subjectivity can lead to different 
interpretations of the same content. Insights from such studies remain 
useful for disciplines beyond Computer Science. This thesis focuses on 
characterizing online platforms (Instagram and Reddit) usage during major 
social events and proposing a method to account for human bias when 
studying such user-generated content.

This thesis contains three main contributions: 1) We study the use of the 
Instagram platform for social activism. We take a case study of social 
unrest and highlight that users circumvent the platform limitations by 
using screenshots and embedding their messages into various symbols. 2) We 
study the surge in WallStreetBets during the GameStop Short Squeeze event 
and explore newly subscribed users' quick adoption of community 
communication norms. 3) Finally, we provide a detailed summary of how the 
human interpretation of any subjective content can be subject to 
comprehension bias; hence, we propose a method to counter this human bias 
with a use case of political content.

This work presents new insights into the users' adoption of the platforms, 
regardless of their primary or commonly perceived goals, and highlights 
the larger potential of such platforms. These insights can be used for 
downstream research, such as prediction tasks, along with the 
generalizability and applicability of the bias detection method in 
real-life crowdsourcing systems.


Date:			Friday, 25 November 2022

Time:                  	3:00pm - 5:00pm

Venue: 			Room 5506
 			Lifts 25/26

Committee Members:	Prof. Pan Hui (Supervisor, EMIA)
 			Dr. Tristan Braud (Supervisor)
 			Prof. James Kwok (Chairperson)
 			Dr. Xiaojuan Ma
 			Dr. Gareth Tyson (IOT)


**** ALL are Welcome ****