TY - GEN
AU - Wagner, Claudia
AU - Singer, Philipp
AU - Strohmaier, Markus
AU - Huberman, Bernardo A.
TI - Semantic stability in social tagging streams
PB - ACM
KW - Semantik
KW - Soziale Medien
KW - Methodenvergleich
KW - Methodologie
KW - Twitter
KW - Ranking
KW - Messung
KW - Simulation
KW - Methodenforschung
KW - Netzgemeinschaft
KW - social tagging
KW - emergent semantics
KW - social semantics
KW - distributional semantics
KW - stabilization process
KW - Stable Tag Proportions
KW - Stable Tag Distributions
KW - Power Law Fits
PY - 2014
N2 - Veröffentlichungsversion
N2 - begutachtet (peer reviewed)
N2 - In: Proceedings of the 23rd International Conference on World Wide Web 2014. 2014. S. 735-746. ISBN 978-1-4503-2744-2
N2 - One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.
BT - Proceedings of the 23rd International Conference on World Wide Web 2014
BT - Interaktive, elektronische Medien
CY - New York
UR - http://slubdd.de/katalog?TN_libero_mab2
ER -
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