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This book constitutes the refereed proceedings of the 8th
International Conference on Computational Data and Social Networks,
CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019.
The 22 full and 8 short papers presented in this book were
carefully reviewed and selected from 120 submissions. The papers
appear under the following topical headings: Combinatorial
Optimization and Learning; Influence Modeling, Propagation, and
Maximization; NLP and Affective Computing; Computational Methods
for Social Good; and User Profiling and Behavior Modeling.
This SpringerBrief brings order to the wealth of research studies
that contribute to shape our understanding of on-line social
networks (OSNs) lurking phenomena. This brief also drives the
development of computational approaches that can be effectively
applied to answer questions related to lurking behaviors, as well
as to the engagement of lurkers in OSNs. All large-scale online
social networks (OSNs) are characterized by a participation
inequality principle, i.e., the crowd of an OSN does not actively
contribute, rather it takes on a silent role. Silent users are also
referred to as lurkers, since they gain benefit from others'
information without significantly giving back to the community.
Nevertheless, lurkers acquire knowledge from the OSN, therefore a
major goal is to encourage them to more actively participate.
Lurking behavior analysis has been long studied in social science
and human-computer interaction fields, but it has also matured over
the last few years in social network analysis and mining. While the
main target audience corresponds to computer, network, and web data
scientists, this brief might also help increase the visibility of
the topic by bridging different closely related research fields.
Practitioners, researchers and students interested in social
networks, web search, data mining, computational social science and
human-computer interaction will also find this brief useful
research material .
The widespread use of XML in business and scientific databases has
prompted the development of methodologies, techniques, and systems
for effectively managing and analyzing XML data. This has
increasingly attracted the attention of different research
communities, including database, information retrieval, pattern
recognition, and machine learning, from which several proposals
have been offered to address problems in XML data management and
knowledge discovery. XML Data Mining: Models, Methods, and
Applications aims to collect knowledge from experts of database,
information retrieval, machine learning, and knowledge management
communities in developing models, methods, and systems for XML data
mining. This book addresses key issues and challenges in XML data
mining, offering insights into the various existing solutions and
best practices for modeling, processing, analyzing XML data, and
for evaluating performance of XML data mining algorithms and
systems.
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