701 research outputs found
Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments
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
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
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
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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