Relational classification has been extensively studied recently due to its applications in social, biological, technological, and information networks. Much of the work in relational learning has focused on analyzing input data that comprise a single network. Although machine learning researchers have considered the issue of how to sample training and test sets from the input network (for evaluation), the mechanisms which are used to construct the input networks have largely been ignored. In most cases, the input network has itself been sampled from a larger target network (e.g., Facebook) and often the researcher is unaware of how the input network was constructed or what impact that may have on evaluation of the relational models. Since the goal in evaluating relational classification algorithms is to accurately assess their performance on the larger target network, it is critical to understand what impact the initial sampling method may have on our estimates of classification accuracy.In this paper, we present different sampling methods and systematically study their impact on evaluation of relational classification. Our results indicate that the choice of sampling method can impact classification performance, and thus consequently affects the accuracy of evaluation.

Although people receive trusted, personalized recommendations and auxiliary social benefits when they ask questions of their friends, using a search engine is often a more effective way to find an answer. Attempts to integrate social and algorithmic search have thus far focused on bringing social content into algorithmic search results. However, more of the benefits of social search can be preserved by reversing this approach and bringing algorithmic content into natural question-based conversations. To do this successfully, it is necessary to adapt search engine interaction to a social context. In this paper, we present SearchBuddies, a system that responds to Facebook status message questions with algorithmic search results. Via a three-month deployment of the system to 122 social network users, we explore how people responded to search content in a highly social environment. Our experience deploying SearchBuddies shows that a socially embedded search engine can successfully provide users with unique and highly relevant information in a social context and can be integrated into conversations around an information need. The deployment also illuminates specific challenges of embedding a search engine in a social environment and provides guidance toward solutions.

Web search engines emerged prior to the dominance of social media. What if we imagined search as integrating with social media from the ground up? So.cl is a web application that combines web browsing, search, and social networking for the purposes of sharing and learning around topics of interest. In this paper, we present the results of a deployment study examining existing learning practices around search and social networking for students, and how these practices shifted when participants adopted So.cl. We found prior to using So.cl that students already heavily employed search tools and social media for learning. With the use of So.cl, we found that users engaged in lightweight, fun social sharing and learning for informal, personal topics, but not for more heavyweight collaboration around school or work. The public nature of So.cl encouraged users to post search results as much for self-expression as for searching, enabling serendipitous discovery around interests.

We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openness by introducing the receptivity of a node. Given a social network and data related to the ordering of adoption of information items by nodes, we develop a maximum-likelihood framework for estimating endogeneity, exogeneity and receptivity parameters. We apply our methodology to synthetic and real data and demonstrate its efficacy as a data-analytic tool.

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.

Social media plays an important role in the process of political engagement, especially in societies where significant constraints over traditional media and participation still exist. Little is known about how social media use is related to these constraints. This study examines how citizens’ perceptions of government control predict social media use and how this use is related to offline participation in the context of a city-state, Singapore. Based on a national survey of 2000 respondents, we found that perceptions of control over traditional media and political activity increase content production on social media and that perceived control of the mass media motivates citizens to consume political content on social media. Interestingly, perceptions of government control over the Internet reduced rather than increased social media production. More importantly, we find that social media use is related to a greater likelihood of offline citizen participation, namely attendance of political rallies. The findings suggest that social media alters the balance of power in the dependency relationships that exist between the government, media organizations and citizens, creating new venues for online political discourse which in turn help promote real-world political participation.

OurCity is a site-specific digital artwork designed to solicit, aggregate and visualize citizens’ views on the cities in which they live. It aims to allow people to have their voice heard in a way which is fun and engaging and reduces the gap between citizens and policymakers. OurCity builds on our previous work, VoiceYourView (Whittle et al 2010) which used similar data aggregation techniques but a completely different visualization of user-generated data. This paper revisits the key results from VoiceYourView and hence uses OurCity as an additional validation exercise to assess whether VoiceYourView results are generalizable.

Do Happy Meals really make us happy? Do salads make us blue? Is cake our comfort? FoodMood is an interactive data visualisation project that gives citizens a rare opportunity to engage and reflect, acknowledge, and understand the connection between emotion, obesity and food. The project explores the opportunities presented by the data-sharing world of today’s cities using global English-language tweets about food coupled with sentiment analysis. It aims to gain a better understanding of global food consumption patterns and its impact on the daily emotional well-being of people against the backdrop of country data such as Gross Domestic Product (GDP) and obesity levels. A key finding is that tweets can be used to find a relationship between certain foods, food sentiment and obesity levels in countries. Overall FoodMood shows a majority positive sentiment towards food. Other findings, although constantly evolving, indicate trends such as: globally meat enjoys a high sentiment rating and is often tweeted about; fast-food companies dominate the food consumption landscapes of most countries’ tweets although not all of them enjoy equal sentiment ratings across countries. Ultimately, FoodMood reveals a hidden layer of meaningful digital, social, and cultural data that provide a basis for further analysis.

This paper reports ongoing work in the “Networks of Texts and People” project, which is developing methods to visualize the social and epistemological contexts of information contained in blogs. Here, we propose an approach to visualize information diffusion and polarization in the blogosphere, with two novel characteristics. Firstly, we demonstrate how text content can be analyzed and visualized as key statements, rather than as keywords. Secondly, we sketch and discuss ideas for a visual analytic tool that integrates data about blog networks with data about the occurrence of related key statements in blog posts.

In this work, we investigated the social media streams to understand their characteristics and their temporal aspects. We assumed that each blogger has different temporal preference for posting. To investigate this hypothesis, we analyzed a massive dataset, nearly 700,000 blog articles, with the consideration of two factors which are day of the week and time of the day. The comparison was done in manifold ways: Blogosphere vs. Twitter, commercial blogs vs. non-commercial blogs, and their individuals. We hope that this work provides a hint to develop a personalized system which can be used for the reduction of the system resources for pull/fetch technology.