Maira Athanazio de Cerqueira Gatti

Large-Scale Multi-agent-Based Modeling and Simulation of Microblogging-Based Online Social Network

Online Social Networks (OSN) are self-organized systems with emergent behavior from the individual interactions. Microblogging services in OSN, like Twitter and Facebook, became extremely popular and are being used to target marketing campaigns. Key known issues on this targeting is to be able to predict human behavior like posting, forwarding or replying a message with regard to topics and sentiments, and to analyze the emergent behavior of such actions. To tackle this problem we present a method to model and simulate interactive behavior in microblogging OSN taking into account the users sentiment. We make use of a stochastic multi-agent based approach and we explore Barack Obama’s Twitter network as an egocentric network to present the experimental simulation results. We demonstrate that with this engineering method it is possible to develop social media simulators using a bottom-up approach (micro level) to evaluate the emergent behavior (macro level) and our preliminary results show how to better tune the modeler and the sampling and text classification impact on the simulation model.

A simulation-based approach to analyze the information diffusion in microblogging online social network

In this paper we propose a stochastic multi-agent based approach to analyze the information diffusion in Microblogging Online Social Networks (OSNs). OSNs, like Twitter and Facebook, became extremely popular and are being used to target marketing campaigns. Key known issues on this targeting is to be able to predict human behavior like posting a message with regard to some topics, and to analyze the emergent behavior of such actions. We explore Barack Obamas Twitter network as an egocentric network to present our simulation-based approach and predictive behavior modeling. Through experimental analysis, we evaluated the impact of inactivating both Obama and the most engaged users, aiming at understanding the influence of those users that are the most likely to disseminate information over the network.

Reaction times for user behavior models in microblogging online social networks

Online Social Networks (OSNs) have, in recent years, emerged as a new way to communicate, diffuse information, coordinate people, establish relationships, among other possibilities. In this context, being able to understand and predict how users behave and developing appropriate models is a key problem to work with OSNs, concerning from marketing campaigns to social movements, for example. Twitter, for instance, was a heavily explored tool in Obamas 2012 election. In this paper, we explore Obamas Twitter network and model its users behavior, applying a stochastic multi-agent based simulation to reproduce the observed data. We study the effects of different time discretizations when applying a first order Markov Model to learn the user behavior and determine that, for Obamas egocentric network, users present a short reaction time to received messages.

A scalable architecture for real-time analysis of microblogging data

As events take place in the real world, e.g., sports games and marketing campaigns, people react and interact on online social networks (OSNs), especially microblog services such as Twitter, generating a large stream of data. Analyzing this data presents an opportunity for researchers and companies to better understand human behavior (both on the network and in real life) during the events lifespan. Designing automated systems to conduct these analyses in fractions of minutes (or even seconds) is subjected to many challenges: the volume of data is large, the number of posts in future events cannot be predicted, and the system need to be always available and running smoothly to avoid information loss and delays on delivering the analytics results. In this paper, we present a scalable architecture for real-time analysis of microblogging data, with the ability to deal with large volumes of posts, by considering modular parallel workflows. This architecture, which has been implemented on the IBM InfoSphere Streams platform, was tested on a real-world use case to conduct sentiment analysis of Twitter posts during the games of the 2013 Federation Internationale de Football Association (FIFA) Confederations Cup, and the system has successfully coped with the challenges of this task.

A learning feature engineering method for task assignment

Multi-domain IT services are delivered by technicians with a variety of expert knowledge in different areas. Their skills and availability are an important property of the service. However, most organizations do not have a consistent view of this information because creation and maintenance of a skill model is a difficult task, especially in light of privacy regulations, changing service catalogs and worker turnover. We propose a method for ranking technicians on their expected performance according to their suitability for receiving the assignment of a service request without maintaining an explicit skill model describing which skills are possessed by each technician. We find appropriate assignees by making use of similarities between the assignees and previous tasks performed by them.

Handling big data on agent-based modeling of online social networks with mapreduce

There is an increasing interest on using Online Social Networks (OSNs) in a wide range of applications. Two interesting problems that have received a lot of attention in OSNs is how to provide effective ways to understand and predict how users behave, and how to build accurate models for specific domains (e.g., marketing campaigns). In this context, stochastic multi-agent based simulation can be employed to reproduce the behavior observed in OSNs. Nevertheless, the first step to build an accurate behavior model is to create an agent-based system. Hence, a modeler needs not only to be effective, but also to scale up given the huge volume of streaming graph data. To tackle the above challenges, this paper proposes a MapReduce-based method to build a modeler to handle big data. We demonstrate in our experiments how efficient and effective our proposal is using the Obamas Twitter network on the 2012 U.S. presidential election.

A learning method for improving quality of service infrastructure management in new technical support groups

Service infrastructure management requires the matching of tasks to technicians with a variety of expert knowledge in different areas. Most Service Delivery organizations do not have a consistent view of the evolution of the technician skills because in a dynamic environment the creation and maintenance of a skill model is a difficult task, especially in light of privacy regulations, changing service catalogs and worker turnover. In addition, as services expand, new technical support groups for the same type of services are created and also new technicians may be added, either into a new group or into existing groups. To tackle this problem we evolve a method for ranking technicians on their expected performance according to their suitability for receiving the assignment of a service request. This method makes use of similarities between the technicians and previous tasks performed by them. We propose a strategy for incorporating new technicians and delivery team reorganizations into the method and we present experimental results demonstrating the efficacy of the strategy. Applying this strategy to new teams yields on average acceptable accuracy within 4 hours, though with a wide variation across teams for the first 12 hours. Accuracy and its variability approach the quality of accuracy on older teams over 24 hours.