Patterns in Knowledge Production

abstract: This dissertation will look at large scale collaboration through the lens of online communities to answer questions about what makes a collaboration persist. Results address how collaborations attract contributions, behaviors that could give rise to patterns seen in the data, and the properties of collaborations that drive those behaviors. It is understood that collaborations, online and otherwise, must retain users to remain productive. However, before users can be retained they must be recruited. In the first project, a few necessary properties of the ``attraction'' function are identified by constraining the dynamics of an ODE (Ordinary Differential Equation) model. Additionally, more than 100 communities of the Stack Exchange networks are parameterized and their distributions reported. Collaborations do not exist in a vacuum, they compete with and share users with other collaborations. To address this, the second project focuses on an agent-based model (ABM) of a community of online collaborations using a mechanistic approach. The ABM is compared to data obtained from the Stack Exchange network and produces similar distributional patterns. The third project is a thorough sensitivity analysis of the model created in the second project. A variance based sensitivity analysis is performed to evaluate the relative importance of 21 parameters of the model. Results indicate that population parameters impact many outcome metrics, though even those parameters that tend towards a low impact can be crucial for some outcomes.Dissertation/ThesisDoctoral Dissertation Applied Mathematics for the Life and Social Sciences 201

An Investigation of Social Support Exchange and Communication Patterns among Chinese on Online Discussion Forum

Online Health Communities (OHCs), frequently adopted as online discussion forums for online users to communicate on health issues, have been used worldwide. By analyzing a representative breast-cancer-related OHC from mainland China—Baidu Discussion Forum, this study attempts to investigate social support exchange and communication patterns through user-generated content by data mining approaches. According to the outcomes, emotional support seeking and providing presents itself to be a more critical theme among Chinese users than other types of social support. In addition, almost half of the users on Baidu Discussion Forum have simple patterns of involvement, and a fairly small proportion of highly active Chinese users are quite influential in shaping the connections of the social support network. Meanwhile, the off-topic discussions which are not directly on health concerns are not frequently touched by Chinese people. This may impact the longevity of both users and threads, and undermine the foundation of OHCs in the long term. The findings have practical implications for researchers and health practitioners targeting on the Chinese population

Online Social Networks: Measurements, Analysis and Solutions for Mining Challenges

In the last decade, online social networks showed enormous growth. With the rise of these networks and the consequent availability of wealth social network data, Social Network Analysis (SNA) led researchers to get the opportunity to access, analyse and mine the social behaviour of millions of people, explore the way they communicate and exchange information. Despite the growing interest in analysing social networks, there are some challenges and implications accompanying the analysis and mining of these networks. For example, dealing with large-scale and evolving networks is not yet an easy task and still requires a new mining solution. In addition, finding communities within these networks is a challenging task and could open opportunities to see how people behave in groups on a large scale. Also, the challenge of validating and optimizing communities without knowing in advance the structure of the network due to the lack of ground truth is yet another challenging barrier for validating the meaningfulness of the resulting communities. In this thesis, we started by providing an overview of the necessary background and key concepts required in the area of social networks analysis. Our main focus is to provide solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive technique to help in the prediction of the execution time of the analysis tasks for evolving networks through employing predictive modeling techniques to the problem of evolving and large-scale networks. Second, we study the performance of existing community detection approaches to derive high quality community structure using a real email network through analysing the exchange of emails and exploring community dynamics. The aim is to study the community behavioral patterns and evaluate their quality within an actual network. Finally, we propose an ensemble technique for deriving communities using a rich internal enterprise real network in IBM that reflects real collaborations and communications between employees. The technique aims to improve the community detection process through the fusion of different algorithms

Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction

Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive sub-websites from the StackExchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive sub-websites. These patterns are mainly: cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactory prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are: (1) the decay process is not governed by only one network measure; it is better described using multiple measures; (2) the expert members of the StackExchange sub-websites were mainly responsible for the activity or inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is going through decay dynamics that may lead to it becoming fully-decayed; and (4) decayed sub-websites were originally less resilient to inactivity decay, unlike the alive sub-websites

Learning networks for professional development:Current research approaches and future trends

Learning networks are technology supported communities through which learners share knowledge with each other and jointly develop new knowledge (Sloep & Berlanga, 2011). This way, learning networks enrich the experience of continuous professional development and lifelong learning. Examples of learning networks for professional development are communities of employees who want to improve customer services, lawyers who want exchange knowledge and experience, and communities of teachers who exchange their experiences and seek for collaboration. Learning networks that support activities for educational professionals is enjoying increasing interest, see for instance Cloudworks (http://cloudworks.ac.uk/), Tapped-In (http://tappedin.org), or eTwinning (www.etwinning.net). However, the full potential and added value of these networks could be maximised if new frameworks, tools and techniques would be developed (Schlager, et al., 2009). A case in point is the European project Teacher’s Lifelong Learning Networks (Tellnet). This project aims to study professional development networks by exploring analysis and visualisation techniques to identify relevant structures and patterns, and to specify performance indicators for facilitating collaboration, innovation and creativity of teachers. Tools are investigated to foster peer-support, collaboration, and increase social capital. Moreover, specific future scenarios on the role of teacher networks for learning are developed, bringing together the evidence found with emerging social and technical trends in Europe. The above mentioned eTwinning network is taken as study case. eTwinning promotes teacher and school collaboration through the use of ICT. It is a large online network (over 150.000 European teachers) in which teachers can work with each other and learn from each other. Through this network, collaborative cross-border school projects can be started on a wide variety of subjects, e.g. having multiple primary school students working together and learning about different cultures. Additionally, teachers can attend a variety of professional development activities, such as online Groups or Learning Labs to improving both personal and professional teaching skills. The aim of this symposium is to present current Tellnet efforts that aim to understand and enhance learning networks for professional development. This includes contributions that attempt to answer questions such as: how network learning can contribute to successful continuous professional development and competence building? How could learning analytics be used in order to identify benefits of learning networks, such as social capital? What will be the role of networks in the coming years? Answering these questions requires a holistic approach that considers pedagogical and technical underpinnings, as well as individual, social and organizational aspects

Quantifying biosynthetic network robustness across the human oral microbiome

Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.https://www.biorxiv.org/content/10.1101/392621v1First author draf

The Analysis of Online Communities using Interactive Content-based Social Networks

published or submitted for publicationis peer reviewe

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14