local/global term analysis for discovering community differences in social networks
DESCRIPTION
Local/Global Term Analysis for Discovering Community Differences in Social Networks. David Fuhry , Yiye Ruan, and Srinivasan Parthasarathy. Data Mining Research Laboratory Dept. of Computer Science and Engineering The Ohio State University. Communities in Social Networks. Observations: - PowerPoint PPT PresentationTRANSCRIPT
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Local/Global Term Analysis for Discovering Community
Differences in Social Networks
David Fuhry, Yiye Ruan, and Srinivasan Parthasarathy
Data Mining Research LaboratoryDept. of Computer Science and Engineering
The Ohio State University
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Communities in Social Networks
Observations:•Social networks consist of many interacting communities of users.•Each community can be characterized by the content which its members generate.
Motivating questions:•Given a community, how can we determine what its members are talking about, relative to the entire social network?•Given two communities, how can we determine the difference between them?
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Methodology• A community’s users mention relevant terms
frequently.
• Many works look at #hashtags or most frequent terms.
• But not all frequent terms are relevant.
• Desiderata:– Consider all content terms
– Interpretable
– scalable to million-user social networks
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Four-step Process• Four-step process for determining community
differences:– Community Discovery
– Term Extraction & Aggregation
– Visualization
– Handling Time Varying Data
Network
Content
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1. Community Discovery (I)
• Keyword search based identification of candidate users
• Extract underlying network of users
• Local community identification• Graph clustering (e.g. METIS
[KARYPIS’99], Graclus [DHILLON’07], MLR-MCL [SATULURI’09], Localized Clustering (L-Spar) [SATULURI’11])
• Modularity [NEWMAN’04]
• Content-Sensitive Viewpoint Neighborhoods [Asur’09]
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1. Community Discovery (II)
• Start with the network of all users
• Extract candidate communities• Using any community discovery
algorithm
• Filter candidate communities by keyword strength
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2. Term Extraction & Aggregation
• Extract terms from each message and weight them
• Term Frequency• TF/IDF• Domain-dependent
semantic importance
• Merge terms• Combine synonyms• Handling hypernyms
• Aggregate them by user
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3. Visualization
• Plot terms by frequency across two axes.
• Global (all users) on Y-axis• Local (community users) on
X-axis.• Terms on the regression line
are equifrequent in both groups
• Terms off the regression line are relatively more frequent in one group
• Support for multiple scales of local community identification
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4. Handling Time Varying Data
• Time range divided into batches• Perform steps 1 to 3 for each batch• Visualize results
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Experimental Results
Between Nikon and Olympus communities, Olympus community talks more about blogs.
Using a dataset of 1M tweets we look at groups discussing Canon, Nikon, and Olympus cameras:
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Experimental Results
Between camera and global communities, camera community talks less about health, teeth, and success.
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Experimental ResultsUsing a dataset of 2M tweets about the “Occupy” movement, we compare “Occupy Oakland” to the entire “Occupy” movement:
Occupy Oakland movement talks less about NYPD, p2 (group of progressives using social media), and tcot (“Top Conservatives On Twitter”).
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Filter and Zoom
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Conclusions• Four-part visual analytic framework for
discovering differences between communities in social networks.– Simple– Scalable
• Qualitative and quantitative results.
• Future– Temporal– More quantitative measures– Automatically determine best scale
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Thank You!