Dynamics of Information Diffusion and Social Sensing

TL;DR

This paper provides a comprehensive tutorial on information diffusion and social sensing in social networks, covering models, algorithms, and experimental insights from diverse fields like network science, economics, and signal processing.

Contribution

It introduces new models and algorithms for social sensing, including diffusion, Bayesian learning, revealed preferences, and time series analysis, with applications to real datasets.

Findings

Diffusion models effectively characterize information spread in social networks.

Bayesian social learning models can predict social sensor behavior in finance.

Time series analysis reveals interaction patterns between social sensors and content creators.

Abstract

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four 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 and risk averse social learning is considered with applications in finance and online reputation systems. Third, 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. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four 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 chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.