Prediction, evolution and privacy in social and affiliation networks

In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal

Public diplomacy: meeting new challenges. Report of Wilton Park Conference 902

What is the role of public diplomacy in the delivery of international objectives? Is there scope for more intergovernmental collaboration? Do governments understand their audiences and how best to reach them? In what ways can practitioners take advantage of new technology? What can be learnt from non-governmental actors? How to ensure sharing of best practice

The Dynamics of Multi-Modal Networks

The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains

The Potential of Social Media Intelligence to Improve Peoples Lives: Social Media Data for Good

In this report, developed with support from Facebook, we focus on an approach to extract public value from social media data that we believe holds the greatest potential: data collaboratives. Data collaboratives are an emerging form of public-private partnership in which actors from different sectors exchange information to create new public value. Such collaborative arrangements, for example between social media companies and humanitarian organizations or civil society actors, can be seen as possible templates for leveraging privately held data towards the attainment of public goals

Scoping a vision for formative e-assessment: a project report for JISC

Assessment is an integral part of teaching and learning. If the relationship between teaching and learning were causal, i. e. if students always mastered the intended learning outcomes of a particular sequence of instruction, assessment would be superfluous. Experience and research suggest this is not the case: what is learnt can often be quite different from what is taught. Formative assessment is motivated by a concern with the elicitation of relevant information about student understanding and / or achievement, its interpretation and an exploration of how it can lead to actions that result in better learning. In the context of a policy drive towards technology-enhanced approaches to teaching and learning, the question of the role of digital technologies is key and it is the latter on which this project particularly focuses. The project and its deliverables have been informed by recent and relevant literature, in particular recent work by Black andIn this work, they put forward a framework which suggests that assessment for learning their term for formative assessment can be conceptualised as consisting of a number of aspects and five keystrategies. The key aspects revolve around the where the learner is going, where the learner is right now and how she can get there and examines the role played by the teacher, peers and the learner. Language: English Keywords: assessments, case studies, design patterns, e-assessmen

Higher Education Exchange: 2009

This annual publication serves as a forum for new ideas and dialogue between scholars and the larger public. Essays explore ways that students, administrators, and faculty can initiate and sustain an ongoing conversation about the public life they share.The Higher Education Exchange is founded on a thought articulated by Thomas Jefferson in 1820: "I know no safe depository of the ultimate powers of the society but the people themselves; and if we think them not enlightened enough to exercise their control with a wholesome discretion, the remedy is not to take it from them, but to inform their discretion by education."In the tradition of Jefferson, the Higher Education Exchange agrees that a central goal of higher education is to help make democracy possible by preparing citizens for public life. The Higher Education Exchange is part of a movement to strengthen higher education's democratic mission and foster a more democratic culture throughout American society.Working in this tradition, the Higher Education Exchange publishes interviews, case studies, analyses, news, and ideas about efforts within higher education to develop more democratic societies

Optimizing citizen engagement during emergencies through use of Web 2.0 technologies

Emergencies and disasters create hardships for citizens. To speed up recovery, local governments need to engage with citizens in an interactive information sharing system to convey information while the incident is still developing and to help mitigate and recover from damages. Lack of effective communication can decrease public trust and engender stress and anxiety of the survivors. As service delivery becomes more complicated during an emergency, responders can also benefit from additional information from the public to increase situational awareness and better understand the challenges facing citizens. This thesis examines emergency information needs, emerging information sharing trends, and the potential homeland security application of Web 2.0 technologies such as wikis, blogs, mashups and text messaging. This thesis examines the use of Web 2.0 technologies during the Southern California wildfires as a case study and interviews top emergency managers throughout the country capturing their insights and opinions about the benefits and pitfalls of incorporating Web 2.0 technologies into existing emergency information sharing systems. Local government agencies, the impacted community, and those outside the immediate area seeking opportunities to assist may be interested in the benefits of context-powered knowledge when collaboration from multiple sources converges to facilitate knowledge used for decision making

COMMUNITY DETECTION AND INFLUENCE MAXIMIZATION IN ONLINE SOCIAL NETWORKS

The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes, yet employ a larger set of key users