701 research outputs found

    Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments

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    This work concerns the analysis of number, sizes and other characteristics of groups identified in the blogosphere using a set of models identifying social relations. These models differ regarding identification of social relations, influenced by methods of classifying the addressee of the comments (they are either the post author or the author of a comment on which this comment is directly addressing) and by a sentiment calculated for comments considering the statistics of words present and connotation. The state of a selected blog portal was analyzed in sequential, partly overlapping time intervals. Groups in each interval were identified using a version of the CPM algorithm, on the basis of them, stable groups, existing for at least a minimal assumed duration of time, were identified.Comment: Gliwa B., Ko\'zlak J., Zygmunt A., Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments, in the K. Aberer et al. (Eds.): SocInfo 2012, LNCS 7710, pp. 475-488, Best Paper Awar

    Bloggers Behavior and Emergent Communities in Blog Space

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    Interactions between users in cyberspace may lead to phenomena different from those observed in common social networks. Here we analyse large data sets about users and Blogs which they write and comment, mapped onto a bipartite graph. In such enlarged Blog space we trace user activity over time, which results in robust temporal patterns of user--Blog behavior and the emergence of communities. With the spectral methods applied to the projection on weighted user network we detect clusters of users related to their common interests and habits. Our results suggest that different mechanisms may play the role in the case of very popular Blogs. Our analysis makes a suitable basis for theoretical modeling of the evolution of cyber communities and for practical study of the data, in particular for an efficient search of interesting Blog clusters and further retrieval of their contents by text analysis

    Strategies for online inference of model-based clustering in large and growing networks

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    In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS359 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    I Appreciate You: A Spectral Reading of SoTL during COVID-19

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    What lives amongst loss? This study employs spectral reading practice to thematically analyze the scholarship of teaching and learning (SoTL) produced within the Canadian blogosphere during the COVID-19 pandemic. Despite the extent of loss that the pandemic brought, the findings of this study reveal that SoTL practitioners continued to embrace positive affectivities and “what works” in their reflective research about the experience of teaching and learning during crisis times. The four revealed themes—endless possibilities, teaching as care, care ethics, and community awe—point towards a hardening disciplinary and methodological characterization of SoTL (or what I refer to as a “SoTL attitude”) that is rooted in qualities of appreciation, generosity, and reparation. Overall, this work contributes to examinations of SoTL as an evolving disciplinary area, providing unique insights into its surprisingly cohesive response to the COVID-19 pandemic
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