Search for: All records

This paper explores the structural characteristics of information dissemination on WhatsApp, focusing particularly on the concepts of "breadth" and "depth." "Breadth" refers to the maximum number of groups to which a message is simultaneously forwarded, while "depth" indicates the maximum number of times a message is forwarded. Using a dataset from 1,600 groups in India comprising over 760,000 messages spanning text, images, and videos, this study employs hashing techniques to track message propagation in a privacy-preserving manner. Analysis of cascade size, breadth, and depth reveals significant trends: text and video messages tend to generate larger cascade sizes compared to images. Contrary to public platforms, depth emerges as the primary driver behind widespread information dissemination (which could be due to WhatsApp's limitations on message broadcasts). Additionally, distinct disparities among message types show depth as the decisive factor in text and video cascades, while both breadth and depth significantly contribute to image cascades. These findings underscore the importance of considering structural nuances in understanding information spread dynamics on private messaging platforms, providing valuable insights for effective dissemination strategies and management in digital communication landscapes. 

We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks. Our novel algorithms extend the existing distributed estimation literature and enable the participating agents to estimate a complete sufficient statistic from private signals acquired offline or online over time and to preserve the privacy of their signals and network neighborhoods. This is achieved through linear aggregation schemes with adjusted randomization schemes that add noise to the exchanged estimates subject to differential privacy (DP) constraints, both in an offline and online manner. We provide convergence rate analysis and tight finite-time convergence bounds. We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent’s sensitivity to their signal and network characteristics. Our algorithms are amenable to dynamic topologies and balancing privacy and accuracy trade-offs. Finally, to supplement and validate our theoretical results, we run experiments on real-world data from the US Power Grid Network and electric consumption data from German Households to estimate the average power consumption of power stations and households under all privacy regimes and show that our method outperforms existing first-order privacy-aware distributed optimization methods. 

In widely used models of biological contagion, interventions that randomly rewire edges (generally making them 'longer') accelerate spread. However, recent work has argued that highly clustered, rather than random, networks facilitate the spread of threshold-based contagions, such as those motivated by myopic best response for adoption of new innovations, norms and products in games of strategic complement. Here we show that minor modifications to this model reverse this result, thereby harmonizing qualitative facts about how network structure affects contagion. We analyse the rate of spread over circular lattices with rewired edges and show that having a small probability of adoption below the threshold probability is enough to ensure that random rewiring accelerates the spread of a noisy threshold-based contagion. This conclusion is verified in simulations of empirical networks and remains valid with partial but frequent enough rewiring and when adoption decisions are reversible but infrequently so, as well as in high-dimensional lattice structures.