Eduarda Mendes Rodrigues

Analyzing (social media) networks with NodeXL

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.

TwitterEcho: a distributed focused crawler to support open research with twitter data

Modern social network analysis relies on vast quantities of data to infer new knowledge about human relations and communication. In this paper we describe TwitterEcho, an open source Twitter crawler for supporting this kind of research, which is characterized by a modular distributed architecture. Our crawler enables researchers to continuously collect data from particular user communities, while respecting Twitters imposed limits. We present the core modules of the crawling server, some of which were specifically designed to focus the crawl on the Portuguese Twittosphere. Additional modules can be easily implemented, thus changing the focus to a different community. Our evaluation of the system shows high crawling performance and coverage.

Do You Know the Way to SNA?: A Process Model for Analyzing and Visualizing Social Media Network Data

Traces of activity left by social media users can shed light on individual behavior, social relationships, and community efficacy. Tools and processes to make sense of social traces are essential for enabling practitioners to study and nurture meaningful and sustainable social interaction. Yet such tools and processes remain in their infancy. This paper describes a study of 15 graduate students who were learning to apply Social Network Analysis (SNA) to data from online communities. Their emergent practices were observed via a pre-post survey, diaries, observations, interviews, analysis of assignments and online class interactions, and a group modeling session. From this in-depth look, we derive the Network Analysis and Visualization (NAV) process model and use it to highlight stages where interaction with peers, experts, and features of the SNA tool were most useful. The important role of visualization in supporting networked thinking was essential, as was the iterative nature of goal formation, data structuring, and data analysis. The paper concludes with a discussion of how the NAV model informs the design of SNA tools and services and supports social media practitioners.

Group-in-a-Box Layout for Multi-faceted Analysis of Communities

Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.

Improving the classification of newsgroup messages through social network analysis

Improving the classification of newsgroup messages through social network analysis. In this paper, we focus on automatic classification of message replies into several types. For representing messages we consider rich feature sets that combine the standard author reply-to network properties with features derived from four additional structures identified in the data: 1) a network of authors who participate in the same threads, 2) network of authors who post similar content, 3) network of threads sharing common authors, and 4) network of content-related threads. For selected newsgroups we train linear SVM classifiers to identify agreement and disagreement with the original message, and question and answer patterns in the threads. We show that the use of newly defined features substantially improves classification of messages in comparison with the SVM model based only on the standard reply-to network.

Model for Voter Scoring and Best Answer Selection in Community Q&A Services

Community Question Answering (cQA) services, such as Yahoo! Answers and MSN QnA, facilitate knowledge sharing through question answering by an online community of users. These services include incentive mechanisms to entice participation and self-regulate the quality of the content contributed by the users. In order to encourage quality contributions, community members are asked to nominate the ‘best’ among the answers provided to a question. The service then awards extra points to the author who provided the winning answer and to the voters who cast their vote for that answer. The best answers are typically selected by plurality voting, a scheme that is simple, yet vulnerable to random voting and collusion. We propose a weighted voting method that incorporates information about the voters’ behavior. It assigns a score to each voter that captures the level of agreement with other voters. It uses the voter scores to aggregate the votes and determine the best answer. The mathematical formulation leads to the application of the Brouwer Fixed Point Theorem which guarantees the existence of a voter scoring function that satisfies the starting axiom. We demonstrate the robustness of our approach through simulations and analysis of real cQA service data.

Social Tagging Behaviour in Community-Driven Question Answering

On-line community services such as Live QnA and Yahoo! Answers enable their members to ask questions and have them answered by the community. The questions are labelled by the users to facilitate search, navigation, and recommendations. In this paper we provide an in-depth analysis of the question labelling practices by contrasting the use of community generated tags in the Live QnA service with the use of topic categories from a fixed taxonomy in the Yahoo! Answers service. We found that community tagging is related to higher levels of social interactions amongst users. Analysis of the most frequently used community tags reveals that active users may establish strong social ties around specific tags. Furthermore, the discriminative value of individual community tags can be low since the corresponding questions may cover a variety of topics. Thus, appropriate care needs to be taken when designing search, browsing, and recommender features for question discovery.

Detection of Web Subsites: Concepts, Algorithms, and Evaluation Issues

Web sites are often organized into several regions, each dedicated to a specific topic or serving a particular function. From a users perspective, these regions typically form coherent sets of pages characterized by a distinct navigation structure and page layout-we refer to them as subsites. In this paper we propose to characterize Web site structure as a collection of subsites and devise a method for detecting subsites and entry points for subsite navigation. In our approach we use a new model for representing Web site structure called Link Structure Graph (LSG). The LSG captures a complete hyperlink structure of a Web site and models link associations reflected in the page layout. We analyze a sample of Web sites and compare the LSG based approach to commonly used statistics for Web graph analysis. We demonstrate that LSG approach reveals site properties that are beyond the reach of standard site models. Furthermore, we devise a method for evaluating the performance of subsite detection algorithms and provide evaluation guidelines.

Determining language variant in microblog messages

It is difficult to determine the country of origin of the author of a short message based only on the text. This is an even more complex problem when more than one country uses the same native language. In this paper, we address the specific problem of detecting the two main variants of the Portuguese language --- European and Brazilian --- in Twitter micro-blogging data, by proposing and evaluating a set of high-precision features. We follow an automatic classification approach using a Naïve Bayes classifier, achieving 95% accuracy. We find that our system is adequate for real-time tweet classification.

Deriving an ontology for knowledge management in collaborative innovation networks

Innovation and knowledge creation are tightly related concepts. Collaborative innovation networks (COINs) are very productive ecosystems for knowledge generation. Acknowledging their relevance for innovation, we propose a novel ontology for knowledge management (KM) in COINs, which builds on COINs related literature and is partially derived from topic-related and enterprise ontologies. The utility of the ontology is illustrated through an application case in a software house. The ontology can be applied in different cases involving KM in COINs, such as support the development of IT-based KM services for knowledge co-creation in COINs or, be used as an auditing tool in collaborative innovation processes.