CHARACTERIZING SOCIAL MEDIA PLATFORMS AND THEIR USERS IN RESPONSE TO REAL-LIFE EVENTS

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis 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, and crises lead to 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 toward specific issues.

There is extensive literature on social media usage and bias mitigation on 
user-generated content across different demographics. However, there needs 
to be more focus on the characterization of non-trivial and dynamic 
content and user behavior. 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 WallStreet- Bets (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 howwe 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 four 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)We characterize Twitter users and their content 
focusing on their follower change during the 2022 Russia-Ukraine crisis. 
4) 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. We also highlight the behavioral 
features that correlate with users’ follower change during the crisis. 
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:			Thursday, 19 January 2023

Time:			2:00pm - 4:00pm

Venue:			Room 3494
 			lifts 25/26

Chairperson:		Prof. Weiping LI (MATH)

Committee Members:	Prof. Pan HUI (Supervisor)
 			Prof. Tristan BRAUD (Supervisor)
 			Prof. Raymond WONG
 			Prof. Dan XU
 			Prof. Yang LU (ECON)
 			Prof. King Wa FU (HKU)


**** ALL are Welcome ****