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Mining divergent opinion trust networks through latent dirichlet allocation
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-4722-0823
2012 (English)In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE , 2012, p. 879-886Conference paper, Published paper (Refereed)
Abstract [en]

While the focus of trust research has been mainly on defining and modeling various notions of social trust, less attention has been given to modeling opinion trust. When speaking of social trust mainly homophily (similarity) has been the most successful metric for learning trustworthy links, specially in social web applications such as collaborative filtering recommendation systems. While pure homophily such as Pearson coefficient correlation and its variations, have been favorable to finding taste distances between individuals based on their rated items, they are not necessarily useful in finding opinion distances between individuals discussing a trending topic, e. g. Arab spring. At the same time text mining techniques, such as vector-based techniques, are not capable of capturing important factors such as saliency or polarity which are possible with topical models for detecting, analyzing and suggesting aspects of people mentioning those tags or topics. Thus, in this paper we are proposing to model opinion distances using probabilistic information divergence as a metric for measuring the distances between people's opinion contributing to a discussion in a social network. To acquire feature sets from topics discussed in a discussion we use a very successful topic modeling technique, namely Latent Dirichlet Allocation (LDA). We use the distributions resulting to model topics for generating social networks of group and individual users. Using a Twitter dataset we show that learned graphs exhibit properties of real-world like networks.

Place, publisher, year, edition, pages
IEEE , 2012. p. 879-886
Keywords [en]
Computational modeling, Analytical models, Twitter, Measurement, Probabilistic logic, Biological system modeling, twitter, trust network, topic models, LDA, opinion mining
National Category
Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-118484DOI: 10.1109/ASONAM.2012.158ISI: 000320443500142Scopus ID: 2-s2.0-84874276274ISBN: 978-1-4673-2497-7 (print)OAI: oai:DiVA.org:kth-118484DiVA, id: diva2:606481
Conference
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012; Istanbul; Turkey; 26 August 2012 through 29 August 2012
Note

QC 20130220

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2018-01-11Bibliographically approved
In thesis
1. Trust-Based User Profiling
Open this publication in new window or tab >>Trust-Based User Profiling
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We have introduced the notion of user profiling with trust, as a solution to theproblem of uncertainty and unmanageable exposure of personal data duringaccess, retrieval and consumption by web applications. Our solution sug-gests explicit modeling of trust and embedding trust metrics and mechanismswithin very fabric of user profiles. This has in turn allowed information sys-tems to consume and understand this extra knowledge in order to improveinteraction and collaboration among individuals and system. When formaliz-ing such profiles, another challenge is to realize increasingly important notionof privacy preferences of users. Thus, the profiles are designed in a way toincorporate preferences of users allowing target systems to understand pri-vacy concerns of users during their interaction. A majority of contributionsof this work had impact on profiling and recommendation in digital librariescontext, and was implemented in the framework of EU FP7 Smartmuseumproject. Highlighted results start from modeling of adaptive user profilesincorporating users taste, trust and privacy preferences. This in turn led toproposal of several ontologies for user and content characteristics modeling forimproving indexing and retrieval of user content and profiles across the plat-form. Sparsity and uncertainty of profiles were studied through frameworksof data mining and machine learning of profile data taken from on-line so-cial networks. Results of mining and population of data from social networksalong with profile data increased the accuracy of intelligent suggestions madeby system to improving navigation of users in on-line and off-line museum in-terfaces. We also introduced several trust-based recommendation techniquesand frameworks capable of mining implicit and explicit trust across ratingsnetworks taken from social and opinion web. Resulting recommendation al-gorithms have shown to increase accuracy of profiles, through incorporationof knowledge of items and users and diffusing them along the trust networks.At the same time focusing on automated distributed management of profiles,we showed that coverage of system can be increased effectively, surpassingcomparable state of art techniques. We have clearly shown that trust clearlyelevates accuracy of suggestions predicted by system. To assure overall pri-vacy of such value-laden systems, privacy was given a direct focus when archi-tectures and metrics were proposed and shown that a joint optimal setting foraccuracy and perturbation techniques can maintain accurate output. Finally,focusing on hybrid models of web data and recommendations motivated usto study impact of trust in the context of topic-driven recommendation insocial and opinion media, which in turn helped us to show that leveragingcontent-driven and tie-strength networks can improve systems accuracy forseveral important web computing tasks.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. p. xi, 48
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:10
Keywords
trust, userprofiling, userprofiles, privacy, interest, socialnetwork, recommendersystems
National Category
Information Systems
Identifiers
urn:nbn:se:kth:diva-118488 (URN)978-91-7501-651-1 (ISBN)
Public defence
2013-03-08, C1 Sal, Electrum, ICT/KTH, Isafjordsgatan 20, Kista, 13:00 (English)
Opponent
Supervisors
Note

QC 20130219

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2018-01-11Bibliographically approved

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Matskin, Mihhail

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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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More styles
Language
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  • Other locale
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Output format
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