Information diffusion in social sensing

Vikram Krishnamurthy; William Hoiles

doi:10.3934/naco.2016017

Article Contents

2016, Volume 6, Issue 3: 365-411. Doi: 10.3934/naco.2016017

This issue Previous Article A new computational strategy for optimal control problem with a cost on changing control Next Article

Information diffusion in social sensing

Vikram Krishnamurthy^1, and
William Hoiles^2,

1.
Department of Electrical and Computer Engineering, Cornell University, NY
2.
Department of Electrical and Computer Engineering, University of British Columbia,, Vancouver, V6T 1Z4

Received: August 2015

Revised: September 2016

Published: September 2016

Abstract / Introduction Related Papers Cited by

Abstract

Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This paper presents a tutorial description of three important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models in online reputation systems are presented. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. All three topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this paper stem from recent developments in computer-science, economics, psychology and electrical engineering.

Keywords:

Diffusion,
Susceptible-Infected-Susceptible (SIS model),
random graphs,
mean field dynamics,
social sampling,
social learning,
reputation systems,
Afriat's theorem,
detecting utility maximizers,
revealed preferences,
potential games.

Mathematics Subject Classification: Primary: 91C99, 91A26, 91A80.

Citation:

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