Abstract-Online social networks have recently become an effective and innovative channel for spreading information and influence among hundreds of millions of end users. Most of prior work either carried out empirical studies or focus on the information diffusion modeling in temporal dimension, little attempt has been given on understanding information diffusion over both temporal and spatial dimensions. In this paper, we propose a Partial Differential Equation (PDE), specifically, a Diffusive Logistic (DL) equation to model the temporal and spatial characteristics of information diffusion. We present the temporal and spatial patterns in a real dataset collected from a social news aggregation site, Digg, and validate the proposed DL equation in terms of predicting the information diffusion process. Our experiment results show that the DL model is able to characterize and predict the process of information propagation in online social networks. For example, for the most popular news with 24,099 votes in Digg, the average prediction accuracy of DL model over all distances during the first 6 hours is 92.08%. To the best of our knowledge, this paper is the first attempt to use PDE-based model to study the information diffusion process in both temporal and spatial dimensions in online social networks.

Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropybased metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.

Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control’s influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.

Abstract-Mathematical modeling is an important approach to study information diffusion in online social networks. Prior studies have focused on the modeling of the temporal aspect of information diffusion. A recent effort introduced the spatiotemporal diffusion problem and addressed the problem with a theoretical framework built on the similarity between information propagation in online social networks and biological invasion in ecology [1]. This paper examines the spatio-temporal characteristics in further depth and reveals that there exist regularities in information diffusion in temporal and spatial dimensions. Furthermore, we propose a simpler linear partial differential equation that takes account of the influence of spatial population density and temporal decay of user interests in the information. We validate the proposed linear model with Digg news stories which received more than 3000 votes during June 2009, and show that the model can describe nearly 60% of the news stories with over 80% accuracy. We also use the most popular news story as a case study and find that the linear diffusive model can achieve an accuracy as high as 97.41% for this news story. Finally, we discuss the potential applications of this model towards finding super spreaders and classifying news story into groups.

The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.