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    Understanding User Behavior in Social Networks Using Quantified Moral Foundations

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    Nokhiz_ku_0099M_15879_DATA_1.pdf (1.688Mb)
    Issue Date
    2018-05-31
    Author
    Nokhiz, Pegah
    Publisher
    University of Kansas
    Format
    89 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
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    Abstract
    Moral inclinations expressed in user-generated content such as online reviews or tweets can provide useful insights to understand users’ behavior and activities in social networks, for example, to predict users’ rating behavior, perform customer feedback mining, and study users' tendency to spread abusive content on these social platforms. In this work, we want to answer two important research questions. First, if the moral attributes of social network data can provide additional useful information about users' behavior and how to utilize this information to enhance our understanding. To answer this question, we used the Moral Foundations Theory and Doc2Vec, a Natural Language Processing technique, to compute the quantified moral loadings of user-generated textual contents in social networks. We used conditional relative frequency and the correlations between the moral foundations as two measures to study the moral break down of the social network data, utilizing a dataset of Yelp reviews and a dataset of tweets on abusive user-generated content. Our findings indicated that these moral features are tightly bound with users' behavior in social networks. The second question we want to answer is if we can use the quantified moral loadings as new boosting features to improve the differentiation, classification, and prediction of social network activities. To test our hypothesis, we adopted our new moral features in a multi-class classification approach to distinguish hateful and offensive tweets in a labeled dataset, and compared with the baseline approach that only uses conventional text mining features such as tf-idf features, Part of Speech (PoS) tags, etc. Our findings demonstrated that the moral features improved the performance of the baseline approach in terms of precision, recall, and F-measure.
    URI
    http://hdl.handle.net/1808/27599
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3824]

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    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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