You can also find all my articles on my Google Scholar profile.

Discovering Patterns of Online Popularity from Time Series

Published in Expert Systems with Applications, 2020

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform, or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multifaceted temporal analysis of the evolution of popular online content…

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Predictability limit of partially observed systems

Published in arXiv, 2020

Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system's predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We quantify the loss of predictability due to sampling, and show that it cannot be recovered by using external si…

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Friendship paradox biases perceptions in directed networks

Published in Nature Communications, 2020

Social networks shape perceptions by exposing people to the actions and opinions of their peers. However, the perceived popularity of a trait or an opinion may be very different from its actual popularity. We attribute this perception bias to friendship paradox and identify conditions under which it appears. We validate the findings empirically using Twitter data. Within posts made by users in our…

Quantifying the Effects of Recommendation Systems

Published in 2019 IEEE International Conference on Big Data (Big Data), 2019

Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedback loop in which user behavior becomes magnified in the algorithmic system. Popular items get recommended more frequently, creating the bias that affects and alters user preferenc…

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Effects of Network Structure on Subjective Preference Diversity

Published in 2019 IEEE International Conference on Big Data (Big Data), 2019

Different social media environments enable different levels of community connectedness, which in turn affects the information that a user is exposed to. In this study, we create an agent-based model to investigate how the different levels of connectedness as well as network structure affects a group's diversity of opinions. In the model, agents are tasked with “liking” or “disliking” a set of obje…

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SAGE: A hybrid geopolitical event forecasting system

Published in IJCAI International Joint Conference on Artificial Intelligence, 2019

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produ…

Price of Anarchy in Algorithmic Matching of Romantic Partners

Published in arXiv, 2019

Algorithmic-matching sites offer users access to an unprecedented number of potential mates. However, they also pose a principal-agent problem with a potential moral hazard. The agent's interest is to maximize usage of the Web site, while the principal's interest is to find the best possible romantic partners. This creates a conflict of interest: optimally matching users would lead to stable coupl…

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Collaboration drives individual productivity

Published in Proceedings of the ACM on Human-Computer Interaction, 2019

How does the number of collaborators affect individual productivity? Results of prior research have been conflicting, with some studies reporting an increase in individual productivity as the number of collaborators grows, while other studies showing that the free-rider effect skews the effort invested by individuals, making larger groups less productive. The difference between these schools of th…

Characterizing activity on the deep and dark web

Published in The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 2019

The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them. These web sites serve as hubs for a variety of illicit activities: to trade drugs, stolen user credentials, hacking tools, and to coordinate attacks and manipulation campaigns. Despite its importance to cyber crime, the d2web has not been s…

A Computational Model of Commonsense Moral Decision Making

Published in AIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

We introduce a computational model for building moral autonomous vehicles by learning and generalizing from human moral judgments. We draw on a cognitively inspired model of how people and young children learn moral theories from sparse and noisy data and integrate observations made from different people in different groups. The problem of moral learning for autonomous vehicles is cast as learning…

Taming the unpredictability of cultural markets with social influence

Published in 26th International World Wide Web Conference, WWW 2017, 2017

Unpredictability is often portrayed as an undesirable outcome of social influence in cultural markets. Unpredictability stems from the “rich get richer” effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social influence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of…

Superintelligence cannot be contained: Lessons from Computability Theory

Published in arXiv, 2016

Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the …

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Interdependent scheduling games

Published in IJCAI International Joint Conference on Artificial Intelligence, 2016

We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently. A player is free to schedule his own services at any time; however, each of these services only begins to accrue reward for the player when all predecessor services, which may or may not be controlled by the same player, have been activated. This model, where play…

Asymptotic optimality of myopic optimization in trial-offer markets with social influence

Published in IJCAI International Joint Conference on Artificial Intelligence, 2016

We study dynamic trial-offer markets, in which participants first try a product and later decide whether to purchase it or not. In these markets, social influence and position biases have a greater effect on the decisions taken in the sampling stage than those in the buying stage. We consider a myopic policy that maximizes the market efficiency for each incoming participant, taking into account th…

Assortment optimization under a multinomial logit model with position bias and social influence

Published in 4or, 2016

Motivated by applications in retail, online advertising, and cultural markets, this paper studies the problem of finding an optimal assortment and positioning of products subject to a capacity constraint in a setting where consumers preferences can be modeled as a discrete choice under a multinomial logit model that captures the intrinsic product appeal, position biases, and social influence. For …

Aligning popularity and quality in online cultural markets

Published in Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016, 2016

Social influence is ubiquitous in cultural markets and plays an important role in recommendations for books, songs, and news articles to name only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability. Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consi…

The benefits of social influence in optimized cultural markets

Published in PLoS ONE, 2015

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appe…

Bargaining mechanisms for one-way games

Published in Games, 2015

We introduce one-way games, a two-player framework whose distinguishable feature is that the private payoff of one (independent) player is determined only by her own strategy and does not depend on the actions taken by the other (dependent) player. We show that the equilibrium outcome in one-way games without side payments and the social cost of any ex post efficient mechanism can be far from the …

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A bargaining mechanism for one-way games

Published in IJCAI International Joint Conference on Artificial Intelligence, 2015

We introduce one-way games, a framework motivated by applications in large-scale power restoration, humanitarian logistics, and integrated supplychains. The distinguishable feature of the games is that the payoff of some player is determined only by her own strategy and does not depend on actions taken by other players. We show that the equilibrium outcome in one-way games without payments and the…

Iterated crowdsourcing dilemma game

Published in Scientific Reports, 2014

The Internet has enabled the emergence of collective problem solving, also known as crowdsourcing, as a viable option for solving complex tasks. However, the openness of crowdsourcing presents a challenge because solutions obtained by it can be sabotaged, stolen, and manipulated at a low cost for the attacker. We extend a previously proposed crowdsourcing dilemma game to an iterated game to addres…

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Practical compressed suffix trees

Published in Algorithms, 2013

The suffix tree is an extremely important data structure in bioinformatics. Classical implementations require much space, which renders them useless to handle large sequence collections. Recent research has obtained various compressed representations for suffix trees, with widely different space-time tradeoffs. In this paper we show how the use of range min-max trees yields novel representations a…

Compressed suffix trees for repetitive texts

Published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012

We design a new compressed suffix tree specifically tailored to highly repetitive text collections. This is particularly useful for sequence analysis on large collections of genomes of the close species. We build on an existing compressed suffix tree that applies statistical compression, and modify it so that it works on the grammar-compressed version of the longest common prefix array, whose diff…