Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We also show that these findings hold true across cultures by analyzing tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.Comment: 17 pages, 8 figure

Monitoring and Modelling of Social Networks

In this thesis we contribute to the understanding of online social networks, temporal networks, and non-equilibrium dynamics. As the title of this work suggests, this thesis is split into two parts, \emph{monitoring} and \emph{modelling} social networks. In the first half we look at current methods for understanding the behaviour and influence of individual users within a social network, and assess their robustness and effectiveness. In particular, we look at the role that the temporal dimension plays on these methods and the various representations that temporal networks can take. We introduce a new temporal network representation which describes a temporal network in terms of node behaviour which we use to characterise individuals and collectives. The new representation is illustrated with examples from the online social network Twitter. We model two particular aspects of social networks in the second half of this thesis. The first model, a generalisation of the popular Voter model, considers the dynamics of two opposite opinions in a heterogeneous society which differ by the resolve of their opinion. The second model investigates how the presence of `anti-bandwagon' agents can prevent the spread of ideas and innovations on a social network, particularly on networks with restrictive topologies. This contribution offers new ways to analyse temporal networks and online social media, and also provokes new and interesting questions for future research in the field

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Enhancing User Experience by Extracting Application Intelligence from Network Traffic

Internet Service Providers (ISPs) continue to get complaints from users on poor experience for diverse Internet applications ranging from video streaming and gaming to social media and teleconferencing. Identifying and rectifying the root cause of these experience events requires the ISP to know more than just coarse-grained measures like link utilizations and packet losses. Application classification and experience measurement using traditional deep packet inspection (DPI) techniques is starting to fail with the increasing adoption of traffic encryption and is not cost-effective with the explosive growth in traffic rates. This thesis leverages the emerging paradigms of machine learning and programmable networks to design and develop systems that can deliver application-level intelligence to ISPs at scale, cost, and accuracy that has hitherto not been achieved before. This thesis makes four new contributions. Our first contribution develops a novel transformer-based neural network model that classifies applications based on their traffic shape, agnostic to encryption. We show that this approach has over 97% f1-score for diverse application classes such as video streaming and gaming. Our second contribution builds and validates algorithmic and machine learning models to estimate user experience metrics for on-demand and live video streaming applications such as bitrate, resolution, buffer states, and stalls. For our third contribution, we analyse ten popular latency-sensitive online multiplayer games and develop data structures and algorithms to rapidly and accurately detect each game using automatically generated signatures. By combining this with active latency measurement and geolocation analysis of the game servers, we help ISPs determine better routing paths to reduce game latency. Our fourth and final contribution develops a prototype of a self-driving network that autonomously intervenes just-in-time to alleviate the suffering of applications that are being impacted by transient congestion. We design and build a complete system that extracts application-aware network telemetry from programmable switches and dynamically adapts the QoS policies to manage the bottleneck resources in an application-fair manner. We show that it outperforms known queue management techniques in various traffic scenarios. Taken together, our contributions allow ISPs to measure and tune their networks in an application-aware manner to offer their users the best possible experience

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity