Magdalini Kardara

Large-scale evaluation framework for local influence theories in Twitter

Abstract Influence theories constitute formal models that identify those individuals that are able to affect and guide their peers through their activity. There is a large body of work on developing such theories, as they have important applications in viral marketing, recommendations, as well as information retrieval. Influence theories are typically evaluated through a manual process that cannot scale to data voluminous enough to draw safe, representative conclusions. To overcome this issue, we introduce in this paper a formalized framework for large-scale, automatic evaluation of topic-specific influence theories that are specialized in Twitter. Basically, it consists of five conjunctive conditions that are indicative of real influence exertion: the first three determine which influence theories are compatible with our framework, while the other two estimate their relative effectiveness. At the core of these two conditions lies a novel metric that assesses the aggregate sentiment of a group of users and allows for estimating how close the behavior of influencers is to that of the entire community. We put our framework into practice using a large-scale test-bed with real data from 75 Twitter communities. In order to select the theories that can be employed in our analysis, we introduce a generic, two-dimensional taxonomy that elucidates their functionality. With its help, we ended up with five established topic-specific theories that are applicable to our settings. The outcomes of our analysis reveal significant differences in their performance. To explain them, we introduce a novel methodology for delving into the internal dynamics of the groups of influencers they define. We use it to analyze the implications of the selected theories and, based on the resulting evidence, we propose a novel partition of influence theories in three major categories with divergent performance.

Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery

In signed social networks, users are connected via directional signed links that indicate their opinions about each other. Predicting the signs of such links is crucial for many real-world applications, such as recommendation systems. The authors mine patterns that emerge frequently in the social graph, and show that such patterns possess enough discriminative power to accurately predict the relationships among social network users. They evaluate their approach through an experimental study that comprises three large-scale, real-world datasets and show that it outperforms state-of-the art methods.

SocIoS API: A Data Aggregator for Accessing User Generated Content from Online Social Networks

Following the boost in popularity of online social networks, both enterprises and researchers looked for ways to access the social dynamics information and user generated content residing in these spaces. This endeavor, however, presented several challenges caused by the heterogeneity of data and the lack of a common way to access them. The SocIoS framework tries to address these challenges by providing tools that operate on top of multiple popular social networks allowing uniform access to their data. It provides a single access point for aggregating data and functionality from the networks, as well as a set of analytical tools for exploiting them. In this paper we present the SocIoS API, an abstraction layer on top of the social networks exposing operations that encapsulate the functionality of their APIs. Currently, the component provides support for seven social networks and is flexible enough to allow for the seamless addition of more.

Social media meta-API: leveraging the content of social networks

Social Network (SN) environments are the ideal future service marketplaces. It is well known and documented that SN users are increasing at a tremendous pace. Taking advantage of these social dynamics as well as the vast volumes, of amateur content generated every second, is a major step towards creating a potentially huge market of services. In this paper, we describe the external web services that SocIoS project is researching and developing, and will support with the Social Media community. Aiming to support the end users of SNs, to enhance their transactions with more automated ways, and with the advantage for better production and performance in their workflows over SNs inputs and content, this work presents the main architecture, functionality, and benefits per external service. Finally, introduces the end user, into the new era of SNs with business applicability and better social transactions over SNs content.

Big IoT and Social Networking Data for Smart Cities

In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.

Clustering Documents using the 3-Gram Graph Representation Model

In this paper we illustrate an innovative clustering method of documents using the 3-Gram graphs representation model and deducing the problem of document clustering to graph partitioning. For the latter we employ the kernel k-means algorithm. We evaluated the proposed method using the Test Collections of Reuters-21578, and compared the results using the Latent Dirichlet Allocation (LDA) Algorithm. The results are encouraging demonstrating that the 3-Gram graph method has much better Recall and F1 score but worse Precision. Further changes that will further improve the results are identified.

Policy Testing in Virtual Environments: Addressing Technical and Legal Challenges

Governments want to improve their policy making process by being able to accurately predict the impact of prospective policy measures to the community. Current e-government tools fail to capture the public opinion as they lack in mass participation. Instead of relying on outdated methods of communicating with the public, governments should embrace Web 2.0 technologies and take advantage of the vast the flows of information available online. In +Spaces, the authors introduce a novel way of accessing and evaluating public opinion by using popular virtual spaces, i.e., 3D Virtual Worlds and Social networks, as testing environments and developing an interface that would allow applications to operate inside them, capturing the reactions of citizens to prospective policies. They present the +Spaces platform giving emphasis on technical challenges such as Virtual Spaces interoperability as well as legal requirements related to processing user created data and how the authors addressed them.

PERIKLIS - Electronic Democracy in the 21st Century

PERIKLIS platform encourages citizen participation and supports sophistication of electronic government services by leveraging the capabilities of location based services, social networks and web 2.0 technologies. In this paper a structured analysis is adopted for identifying the advantages, from the digital transformation of the government transactions and the electoral processes, exploring the notion of society members and the benefits for better life conditions through electronic transactions in a Municipality. Adopting a computing approach for e-government and voting methodologies with an easy setup and completion by its members is investigated, reviewing the availability of services through mobile and web based systems, coupled with geo location services. Furthermore, PERIKLIS proposes a high level e-governance and e-voting solution for a municipality while investigating issues that require further research for exploitation and interoperability with more than one Municipalities.