Online Social Media
Digital Fingerprints of Cognitive Reflection
- M. Mosleh, G. Pennycook, and D.G. Rand “Digital Fingerprints of Cognitive Reflection: Cognitive Style and Content Shared on Twitter’’.
- M. Mosleh, G. Pennycook, and D.G. Rand “Self-reported Willingness to Share Political News Articles in Online Surveys Correlates with Actual Sharing on Twitter’’ PsyArXiv.
- Ziv Epistein*, M. Mosleh*, A. Arechar G. Pennycook, and D.G. Rand “Primed for Accuracy: A Cognitive Approach to Fighting Misinformation on Twitter’’.
Experimental Social Networks
Information Gerrymandering and Voting Coordination
We devised an online environment that enhances simultaneous interaction between individuals. We recruit players from online labour market (e.g., Mechanical Turk) and assign them to nodes on a social network through which they interact and receive information from other players and/or programmed AI players. Players receive different payoffs based on the collective outcome of the game. We developed a mathematical model to predict the outcome of the game for different parameters (incentive ratio, assortment within the social network,…). We use the data from experiments to update the mathematical model in an iterative process.
- A. J. Stewart, M. Mosleh, M. Diakonova, A. Arechar, D. G. Rand, J. B. Plotkin “Information Gerrymandering and Undemocratic Decisions”.
Computational Social Networks
Co-evolution of Cognition and Social Norms on Structured Population
We interact and learn from our friends and they do the same with their own friends. Hence, the structure of social network play a key rule on the way we learn and adopt new behavior. We devised a multi-layered model that combines local agent interactions with social learning, thus enables both strategic behavior as well as diffusion of successful strategies within social network.
- M. Mosleh, K. Kyler, J.D Cohen, and D.G. Rand “Population Dynamic of Dual-process Agents on Networks”.
- M. Mosleh, D.G. Rand, “Population Structure Promotes the Evolution of Intuitive Cooperation and Inhibits Deliberation”. Scientific Reports, Nature Publishing Group, Vol. 8 No. 1 2018
- M. Mosleh, B. Heydari, “Fair Topologies: Community Structures and Network Hubs drive Emergence of Fairness Norms” Scientific Reports, Nature Publishing Group, Vol. 7 No. 1 2017.
Natural Language Processing
Consumers Preferences in Airbnb vs. Tripadviser
Peer-to-peer platforms, collectively known as Sharing Economy, are becoming an inventible part of our daily economic activities and, in many cases, are substituting our usage of traditionally provided services by well-established firms. We investigate users’ opinion trends in a popular sharing economy platform for short term property rental (Airbnb) and compare it with a traditional platform for hotel reservation (TripAdvisor). We take a text analytics approach, extract opinion trends from online reviews, and compare them across different aspects of the service. We show that co-variation of opinion trends is a function of location and different aspects, which suggest a new way of market segmentation.
- M. Mosleh and B. Heydari, “Co-evolution of Consumers Preferences in Sharing Economy and Traditional Platforms: the Case of Hospitality Industry” 3rd Annual International Conference on Computational Social Science (IC2S2). 2017, Cologne, Germany.
- M. Mosleh and B. Heydari, “Sharing Economy vs. Conventional Platforms: A Natural Language Processing Approach ” Workshops on Natural Language Processing and Computational Social Science at Annual Meeting of the Association for Computational Linguistics (ACL) 2017, Vancouver, Canada.
Efficient and Stable Network Structures
There are many social, economic, and technical situations where connection between two nodes in the network incurs a cost, yet facilitates overall resource access through out a network and comes with a benefit. Building upon economic network models, we found analytical solution for efficient and stable network structures and a wide range of cost and benefit functions.
- M. Mosleh, P. Ludlow, B. Heydari, “Distributed Resource Management in System of Systems: An Architecture Perspective” Systems Engineering,Vol. 19 No. 4 2018 P 362-374
- B. Heydari, P. Heydari, M. Mosleh, “Efficient Integration in Multi-Community Networks”[working paper], Social Science Research Network.
- B. Heydari, M. Mosleh, K. Dalili, “Efficient Network Structures with Separable Heterogeneous Connection Costs” Economics Letters, vol. 134, no. September, pp. 82-85, 2015.
Computational Models for Networked Systems Design
Distributed and networked architecture increases system flexibility and its responsiveness to environment uncertainties. Yet, an important question is how much flexibility is required given an uncertainty profile of the environment. We have developed an architecture framework that guides decisions about the level of modularity and distribution of networked systems and demonstrated it’s applicability to real world complex systems.
- M. Mosleh, K. Dalili, B. Heydari, “Distributed or Monolithic? A Computational Architecture Decision Framework” IEEE Systems Journal, Vol. 12, No. 1, March 2018 P 125 – 136.
- M. Mosleh, K. Dalili, B. Heydari, “Optimal Modularity for Fractionated Spacecraft: The Case of System F6.”Procedia Computer Science, 28 (2014): 164-170.
- B. Heydari, M. Mosleh, K. Dalili, “From Modular to Distributed Open Architectures: A Unified Decision Framework” ,Systems Engineering, Vol. 19 No. 3 2016 P 252-266
Data-driven Policy Analysis
Spatial Diffusion of Behavioral risk
By considering certain types of risk as social construct that emerge through social interactions over time, we study spatial diffusion of risky behavior in various geographic regions to study how changes in risky behavior in a region affect other regions. As the first step, we study diffusion of risky driving behavior among teenage drivers in the time period between 2000-2014 in the US.
- B. Heydari, M. Mosleh, “Spatial Risk Diffusion: Predicting the propagation of risk linked to human behavior” Point of View, Accenture company.
Post Obama-Care Insurance Competition
One of the main goals of affordable-care act was triggering more competition among health insurance providers. We study the evolution of competition in health insurance market after introduction of ACA by studying the structural evolution of the network that connects participating insurance companies based on the similarity of their plans in similar regions. We demonstrate that this using network measures is a good predictor of evolution of competition in such markets.
- D. Gianetto, M. Mosleh, B. Heydari, “Dynamic Structure of Competition Network in Affordable Care Act Insurance Market”, IEEE Access, 2018.