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    LARGE-SCALE NETWORK ANALYSIS FOR ONLINE SOCIAL BRAND ADVERTISING

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    Large-Scale Network Analysis for ...
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    Author
    Zhang, Kunpeng
    Bhattacharyya, Siddhartha
    Ram, Sudha
    Affiliation
    Univ Arizona, Eller Coll Management, Dept MIS
    Issue Date
    2016-12
    Keywords
    Online advertising
    brand-brand networks
    community detection
    audience selection
    sentiment analysis
    
    Metadata
    Show full item record
    Publisher
    SOC INFORM MANAGE-MIS RES CENT
    Citation
    Zhang, Kunpeng; Bhattacharyya, Siddhartha; and Ram, Sudha. 2016. "Large-Scale Network Analysis for Online Social Brand Advertising," MIS Quarterly, (40: 4) pp.849-868.
    Journal
    MIS QUARTERLY
    Rights
    Copyright © 2016 by the Management Information Systems Research Center (MISRC) of the University of Minnesota.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    This paper proposes an audience selection framework for online brand advertising based on user activities on social media platforms. It is one of the first studies to our knowledge that develops and analyzes implicit brand-brand networks for online brand advertising. This paper makes several contributions. We first extract and analyze implicit weighted brand-brand networks, representing interactions among users and brands, from a large dataset. We examine network properties and community structures and propose a framework combining text and network analyses to find target audiences. As a part of this framework, we develop a hierarchical community detection algorithm to identify a set of brands that are closely related to a specific brand. This latter brand is referred to as the "focal brand." We also develop a global ranking algorithm to calculate brand influence and select influential brands from this set of closely related brands. This is then combined with sentiment analysis to identify target users from these selected brands. To process large-scale datasets and networks, we implement several MapReduce-based algorithms. Finally, we design a novel evaluation technique to test the effectiveness of our targeting framework. Experiments conducted with Facebook data show that our framework provides significant performance improvements in identifying target audiences for focal brands.
    Note
    60 month embargo; Published: Dec 2016
    ISSN
    0276-7783
    Version
    Final published version
    Additional Links
    http://aisel.aisnet.org/misq/vol40/iss4/5/
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    UA Faculty Publications

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