<div class="csl-bib-body">
<div class="csl-entry">Neidhardt, J. (2016). <i>Modeling and understanding social influence in groups and networks</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.37208</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2016.37208
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/2430
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dc.description
Zusammenfassung in deutscher Sprache
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dc.description.abstract
Social influence occurs when a person changes her behavior according to the behavior of other people in the social system. Today, these complex mechanisms can be studied by making use of vast amounts of detailed data on human behavior and social interactions, coming from the World Wide Web and other data sources. The main objective of this work is to capture social influence processes in computational models on a large scale. In the presented analysis, three levels of information are distinguished (i.e., individual, group and network level). To illustrate each level in detail and to show their differences, conventional methods and their shortcomings are discussed and empirical studies are conducted. At the individual level, regression models are applied; at the group level, approaches based on geometric data analysis; and at the network level, social network analysis. At the network level, conditional random field models are introduced as an alternative way to capture social influence processes. Finally, it is discussed, how all three levels can be integrated into one model. The empirical analyses are related to travel recommender systems, churn behavior, sentiments in online forums and team-vs-team competitions. The results of this belong to two categories: 1) methodological advances; 2) concrete statements in different domains of application. It is shown that the introduced models are able to capture social context in an accurate way. Most of them, moreover, scale well. Furthermore, integrating different levels of information allows comparing them and their associations with the studied social influence processes directly. Thus, a more comprehensive picture of the respective domain of application is obtained.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Social Influence
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dc.subject
Social Network Analysis
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dc.subject
Statistical Network Models
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dc.subject
Data Analysis
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dc.subject
Computational Social Science
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dc.subject
Web Science
en
dc.subject
Geometric Data Analysis
en
dc.subject
Picture-Based Recommender Systems
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dc.subject
Virtual Communities
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dc.subject
Team Performance
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dc.title
Modeling and understanding social influence in groups and networks
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2016.37208
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Julia Neidhardt
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E188 - Institut für Softwaretechnik und Interaktive Systeme
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC13103322
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dc.description.numberOfPages
159
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-2285
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0001-7184-1841
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.mimetype
application/pdf
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item.openairetype
doctoral thesis
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item.grantfulltext
open
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.cerifentitytype
Publications
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crisitem.author.dept
E194-04 - Forschungsbereich E-Commerce
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crisitem.author.orcid
0000-0001-7184-1841
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering