Modeling online user interactions and their offline effects on socio-technical platforms

Abstract

Do online interactions trigger reactions back in the offline world? How can these reactions be detected and quantified? Specifically, what insights can be extracted for users, platform owners, and policymakers to minimize the potential harm of such reactions? Society functions based on the complex interactions between individuals, communities, and organizations. We communicate with each other to build family, friendship, and romantic relationships; to seek or provide advice and education; to execute trade and commerce. People unite to form organizations that drive economic activity, govern states, and provide social benefits. The advent of the Internet has enabled these interactions to move online. A website or an application that facilitates the digitization of social interactions is called a socio-technical platform. For instance, individuals converse with each other via direct messaging applications (e.g., WhatsApp, Telegram), share thoughts, and gather feedback from communities (e.g., Reddit, Twitter, Youtube). Trade of goods occurs via e-commerce (e.g., Flipkart, Amazon) and online marketplaces (e.g., Google Play store). At times interactions happening in the online world, trigger reactions in the offline world, which we call overflow. Such overflows can have either a positive or negative impact. Socio-technical platforms save every interaction and associated metadata, providing a unique opportunity to analyze rich data at scale. Discover interaction patterns, detect and quantify overflow of interactions, and extract insights for users and policymakers. This thesis aims to study the interactions by keeping the individual as the focal point. We focus on three broad forms of interactions - i) the effect online community feedback can have on individual offline actions, ii) organizations leveraging individual customers’ online presence to optimize business processes, and iii) how data from tracking platforms can be used to uncover the strategies behind successful users. In the first part, we work on three scenarios - (a) How does community feedback affect an individual future drug consumption frequency in a drug community forum?; (b) What changes does an individual undergo immediately after getting sudden popularity in Online social media? What actions help in maintaining popularity for longer?; (c) Dynamics of interactions in an online COVID-19 support group and what affects a user’s longevity in the community. In the second part, we leverage online information about a user to improve the prediction of Return-to-Origin 1 orders in the e-commerce platform. Finally, in the third part, we leverage data from a habittracking platform to unveil what user actions lead to success in habit-building pursuits.