165 citations · 277 across the 21 of their papers we have counts for
22 papers
Pairs Trading Using a Novel Graphical Matching Approach
Khizar Qureshi, Tauhid Zaman
Pairs trading, a strategy that capitalizes on price movements of asset pairs driven by similar factors, has gained significant popularity among traders. Common practice involves se…
Social Media Engagement and Cryptocurrency Performance
Khizar Qureshi, Tauhid Zaman
We study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discuss…
Bots, Disinformation, and the First Trump Impeachment
Michael Rossetti, Tauhid Zaman
Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during…
The Impact of COVID-19 on Sports Betting Markets
Khizar Qureshi, Tauhid Zaman
We investigate the impact of the COVID-19 pandemic on the betting markets of professional and college sports. We find that during the pandemic, the moneyline betting markets of the…
Optimal Dispatch in Emergency Service System via Reinforcement Learning
Cheng Hua, Tauhid Zaman
In the United States, medical responses by fire departments over the last four decades increased by 367%. This had made it critical to decision makers in emergency response departm…
Mitigating the Backfire Effect Using Pacing and Leading
Qi Yang, Khizar Qureshi, Tauhid Zaman
Online social networks create echo-chambers where people are infrequently exposed to opposing opinions. Even if such exposure occurs, the persuasive effect may be minimal or nonexi…
Detecting Bots and Assessing Their Impact in Social Networks
Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouji +1
Online social networks are often subject to influence campaigns by malicious actors through the use of automated accounts known as bots. We consider the problem of detecting bots i…
Optimizing Opinions with Stubborn Agents
D. Scott Hunter, Tauhid Zaman
We consider the problem of optimizing the placement of stubborn agents in a social network in order to maximally influence the population. We assume the network contains stubborn u…
Detecting Influence Campaigns in Social Networks Using the Ising Model
Nicolas Guenon des Mesnards, Tauhid Zaman
We consider the problem of identifying coordinated influence campaigns conducted by automated agents or bots in a social network. We study several different Twitter datasets which…
Penetrating a Social Network: The Follow-back Problem
Fanyu Que, Krishnan Rajagopalan, Tauhid Zaman
Modern threats have emerged from the prevalence of social networks. Hostile actors, such as extremist groups or foreign governments, utilize these networks to run propaganda campai…
Building a Location-Based Set of Social Media Users
Christopher Marks, Tauhid Zaman
In many instances one may want to gain situational awareness in an environment by monitoring the content of local social media users. Often the challenge is how to build a set of u…
Picking Winners: A Data Driven Approach to Evaluating the Quality of Startup Companies
David Scott Hunter, Ajay Saini, Tauhid Zaman
We consider the problem of evaluating the quality of startup companies. This can be quite challenging due to the rarity of successful startup companies and the complexity of factor…
Finding Online Extremists in Social Networks
Jytte Klausen, Christopher Marks, Tauhid Zaman
Online extremists in social networks pose a new form of threat to the general public. These extremists range from cyberbullies who harass innocent users to terrorist organizations…
Learning Preferences and User Engagement Using Choice and Time Data
Zhengli Wang, Tauhid Zaman
Choice decisions made by users of online applications can suffer from biases due to the users' level of engagement. For instance, low engagement users may make random choices with…
A Multi-Urn Model for Network Search
Christopher E. Marks, Tauhid Zaman
We consider the problem of finding a specific target individual hiding in a social network. We propose a method for network vertex search that looks for the target vertex by sequen…
Predicting Performance Under Stressful Conditions Using Galvanic Skin Response
Carter Mundell, Juan Pablo Vielma, Tauhid Zaman
The rapid growth of the availability of wearable biosensors has created the opportunity for using biological signals to measure worker performance. An important question is how to…
Picking Winners in Daily Fantasy Sports Using Integer Programming
David Scott Hunter, Juan Pablo Vielma, Tauhid Zaman
We consider the problem of selecting a portfolio of entries of fixed cardinality for contests with top-heavy payoff structures, i.e. most of the winnings go to the top-ranked entri…
A Bayesian approach for predicting the popularity of tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow
We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its r…
Twitter event networks and the Superstar model
Shankar Bhamidi, J. Michael Steele, Tauhid Zaman
Condensation phenomenon is often observed in social networks such as Twitter where one "superstar" vertex gains a positive fraction of the edges, while the remaining empirical degr…
Finding Rumor Sources on Random Trees
Devavrat Shah, Tauhid Zaman
We consider the problem of detecting the source of a rumor which has spread in a network using only observations about which set of nodes are infected with the rumor and with no in…
Leaders, Followers, and Community Detection
Dhruv Parthasarathy, Devavrat Shah, Tauhid Zaman
Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding…
Rumors in a Network: Who's the Culprit?
Devavrat Shah, Tauhid Zaman
We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with a variant of the popular SIR model and then c…