07/18/2022
By Mary Lou Kelly

The Department of Operations and Information Systems at the Manning School of Business invites you to attend a doctoral dissertation proposal by Sambit Tripathi on “Analysis of User Generated Content in Digital Platforms”

Name: Sambit Tripathi
Date: Thursday, July 28, 2022
Time: 9 to 11 a.m.
Location: Virtual via Zoom 

Thesis/Dissertation Title: Analysis of User Generated Content in Digital Platforms

Committee Members

  • Amit Deokar (Co-chair), Operations & Information Systems, Manning School of Business, UMass Lowell
  • Xiao-bai (Bob) Li (Co-chair), Operations & Information Systems, Manning School of Business, UMass Lowell
  • Shakil Quayes, Economics Department, College of Fine Arts Humanities & Social Sciences, UMass Lowell
  • Prasanna Karhade, Department of Information Technology Management, Shidler College of Business, University of Hawai’i at Mānoa

Abstract:
This dissertation proposal focuses on applying data analysis methods on user generated content (UGC) produced on digital platforms. The objective of this dissertation is to: (1) Analyze the order effect of online review text on e-commerce platforms, (2) Understand peer endorsements among gig workers in online labor platforms, and (3) Develop an interpretable item recommendation approach by using customer transactions in online retail platforms.
User-generated content (UGC) is a unique attribute of the Internet that influences individuals’ or organizations’ behavior on digital platforms. Some examples of UGC are retail transactions, social media posts or activities, online reviews, job posts, and video content among others. Social media, e-commerce, online labor markets, and other digital platforms rely on both generation and consumption of UGC to survive in the industry.
The first essay (Chapter 1) focuses on online review text on e-commerce platforms. Online reviews aim to help prospective buyers in their decision-making. Using a large dataset, we extract sentiments, along with novel attributes like product usage contexts and product features present in each online review and analyze their pattern over the order of the review. The second essay (Chapter 2) focuses on peer endorsements among workers in online labor platforms. We apply social value orientation theory to understand the impact of endorsements on workers’ future gig performance. We also understand the factors that influence the generation of endorsements among workers. In the third essay (Chapter 3), we aim to develop an explainable retail item recommendation approach using topic modeling and word embeddings on historical retail transactions of an e-commerce platform.

All interested students and faculty members are invited to attend.