Labeling Actors and Uncovering Causal Accounts of Their States in Social Networks and Social ...

125 Pages·2016·5.94 MB·English

by  Ngot P. Bui

The Pennsylvania State University
The Graduate School
College of Information Sciences and Technology
LABELING ACTORS AND UNCOVERING CAUSAL ACCOUNTS
OF THEIR STATES IN SOCIAL NETWORKS AND SOCIAL
MEDIA
A Dissertation in
Information Sciences and Technology
by
Ngot P. Bui
© 2016 Ngot P. Bui
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
December 2016
The dissertation of Ngot P. Bui was reviewed and approved∗ by the following:
Vasant G. Honavar
Professor of the College of Information Sciences and Technology
Dissertation Advisor, Chair of Committee
C. Lee Giles
Professor of the College of Information Sciences and Technology
Ping Li
Professor of Psychology and Linguistics
Dashun Wang
Assistant Professor of the College of Information Sciences and Technology
John Yen
Professor of the College of Information Sciences and Technology
Andrea H. Tapia
Associate Professor of the College of Information Sciences and Technology
Director of Graduate Program
∗Signatures are on file in the Graduate School.
ii
Abstract
The emergence of social networks and social media has resulted in exponential
increase in the amount of data that link diverse types of richly structured digital
objects e.g., individuals, articles, images, videos, music, etc. Such data are naturally
represented as heterogeneous networks with multiple types of objects e.g., actors,
video,postings,images,etc. andmultipletypesoflinkse.g.,linksthatconnectactors
to items e.g., photos, videos, articles that denote relationships between individuals
and items, and links between actors that denote social ties e.g., friendship, etc. Such
data present a number of research challenges in machine learning, network analysis,
and causal discovery. The data are incomplete, heterogeneous, highly sparse, and
exhibit complex relationships and temporal structure. It is often useful to associate
with each actor or individual, one or more labels that represent the memberships
of the actor in specific groups e.g., political, social, or religious groups. Labels of
actors in a social network can be used in a variety of ways: recommending specific
items (e.g., music, movies), activities. Furthermore, labels or states of actors change
over time due to the temporal nature of the real-world social networks and social
media. Uncovering the causal accounts of the state dynamics of actors has an
important impact in explaining why people change their preference, emotion, or
interest in a specific activity and in predicting actors’ future behaviors. Against this
background, this dissertation aims to develop (i) novel machine learning approaches
for labeling actors in social networks, with particular emphasis on methods for
dealing with a diversity of actors, objects, and relationships and with data sparsity;
(ii) uncovering the causal accounts of the social network and social media dynamics
(with emphasis on causal accounts of sentiment change and related applications).
The introduced algorithms are implemented and experimentally evaluated on a
number of real-world data sets. The proposed methodologies for actor labeling and
analysis of causal accounts have broad applicability in a variety of areas such as
bioinformatics and neuroscience.
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Table of Contents
List of Figures vii
List of Tables ix
Acknowledgments xi
Chapter 1
LabelingActorsandUncoveringCausalAccountsofTheirStates
in Social Networks and Social Media: An Overview 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Dissertation Contributions . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter 2
On the Utility of Abstraction in Labeling Actors in Social Net-
works 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Labeling Actors in Social Networks . . . . . . . . . . . . . . . . . . 13
2.3.1 Constructing Item Type Abstraction Hierarchies . . . . . . . 13
2.3.2 Representing Actors in Social Networks . . . . . . . . . . . . 15
2.3.3 Learning to Label Actors . . . . . . . . . . . . . . . . . . . . 15
2.4 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 Social Media Data . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 Predictive Models . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 22
iv
Chapter 3
LabelingActorsinSocialNetworksUsingaHeterogeneousGraph
Kernel 24
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Heterogeneous Graph Kernel . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 Kernel Function . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 Efficient Computation . . . . . . . . . . . . . . . . . . . . . 28
3.3.3 Learning to Label Actors in Social Network . . . . . . . . . 28
3.4 Experimental Settings and Results . . . . . . . . . . . . . . . . . . 29
3.4.1 Social Media Data . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.4.3 Experimental Design and Results . . . . . . . . . . . . . . . 30
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Chapter 4
Labeling Actors in Multi-view Social Networks by Integrating
Information From Within and Across Multiple Views 35
4.1 Background and Introduction . . . . . . . . . . . . . . . . . . . . . 36
4.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2 Integrating Information from Within and Across Views in Multi-
View Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.1 Labeling Actors in Multi-View Networks . . . . . . . . . . . 39
4.2.2 Random Walk Kernels . . . . . . . . . . . . . . . . . . . . . 40
4.2.3 Inter-Graph Random Walk Kernel . . . . . . . . . . . . . . . 41
4.2.4 Efficient Computation of IRWK . . . . . . . . . . . . . . . . 45
4.3 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3.1 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3.2 Baseline methods . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3.3 IRWK Compared to Baseline Methods . . . . . . . . . . . . 53
4.3.3.1 Labeling Actors Based on Single Views . . . . . . . 53
4.3.3.2 Labeling Actors Based on all Views . . . . . . . . . 54
4.3.4 Parameter Sensitivity . . . . . . . . . . . . . . . . . . . . . . 56
4.3.5 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4 Summary and Future Work . . . . . . . . . . . . . . . . . . . . . . 57
v
Chapter 5
Temporal Causality of Social Support in an Online Community
for Cancer Survivors 59
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2 Temporal Causality . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.3.1 Sentiment Classification of CSN Posts . . . . . . . . . . . . 64
5.3.2 CSN Thread Viewed as a Sequence of Sentiments . . . . . . 65
5.3.3 Probabilistic Kripke Structure for CSN Sentiment Dynamics 68
5.4 Temporal Causality of Sentiments in CSN . . . . . . . . . . . . . . 69
5.4.1 Prima Facie Cause . . . . . . . . . . . . . . . . . . . . . . . 69
5.4.2 Assessing Causal Significance . . . . . . . . . . . . . . . . . 70
5.5 Summary and Future Work . . . . . . . . . . . . . . . . . . . . . . 71
Chapter 6
IncorporatingUncertaintyofActorStatesIntoTemporalCausal-
ity Analysis 74
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.2 Temporal Causality as a function of Discussion Topic . . . . . . . . 77
6.3 Sensitivity of Inferred Temporal Causality of CSN Sentiment Dy-
namics With Respect to the Choice of Classification Threshold . . . 81
6.4 Incorporating Sentiment Classification Error Rates into the Analysis
of Temporal Causality of CSN Sentiment Dynamics . . . . . . . . . 84
6.5 Robustness of Inferred Temporal Causality of CSN Sentiment Dy-
namics With Respect to the Choice of Sentiment Classifiers . . . . . 88
6.6 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 89
Chapter 7
Conclusions and Future Directions 92
7.1 Summary and Contributions . . . . . . . . . . . . . . . . . . . . . . 92
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Appendix A
Derivation of Matrix Form, Convergence in Sections 3.3.2, 4.2.3,
4.2.4 98
A.1 Matrix Form for HGK . . . . . . . . . . . . . . . . . . . . . . . . . 98
A.2 Derivation for IRWK . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Bibliography 103
vi
List of Figures
2.1 Abstraction hierarchies of item type Music and Video, respectively. 11
2.2 Accuracy of classifiers that exploit abstraction and classifiers that
do not exploit abstraction on two real data sets Flickr (18,021 users)
and Last.fm (45,139 users). Figures 2.2a, 2.2b, and 2.2c: accuracies
on Flickr of Iterative classification Naïve Bayes Classifier (INBC),
Iterative classification Logistic Regression Classifier (ILRC), and
EdgeCluster, respectively. Figures 2.2d, 2.2e, and 2.2f: accuracies
on Last.fm of INBC, ILRC, and EdgeCluster, respectively. . . . . . 19
2.3 Performance comparisons of six classifiers based on two variants
of actor representation, Binary (Multivariate) and Count (Multi-
nomial). Figures 2.3a and 2.3b: accuracies on Flickr with Binary
and Count. Figures 2.3c and 2.3d: accuracies on Last.fm with
Binary and Count. Figures 2.3e and 2.3f: macro-F1 measurements
on Flickr with Binary and Count. Figures 2.3g and 2.3h: macro-F1
measurements on Last.fm with Binary and Count. . . . . . . . . . . 20
3.1 Accuracies of six methods on Last.fm and on Flickr . . . . . . . . . 31
3.2 Sensitivity of HGK when changing w : w . . . . . . . . . . . . . . 33
1 2
4.1 Multi-view network. Solid lines between nodes within each view
denote one type of relationship, e.g., friendship. Dotted lines be-
tween nodes in different views encode correspondences between
representations of specific actors across different views. . . . . . . . 38
4.2 Reduction on dimension of T . . . . . . . . . . . . . . . . . . . . . 44
×
4.3 Sensitivity of IRWK w.r.t Stop Probability . . . . . . . . . . . . . . 54
4.4 Sensitivity of IRWK w.r.t Inter-view Transition Probability . . . . . 56
4.5 Kernel Convergence w.r.t Length of Walk on Flickr . . . . . . . . . 57
5.1 Classifying Sentiments of CSN Posts . . . . . . . . . . . . . . . . . 64
5.2 ROC of Adaboost Classifier . . . . . . . . . . . . . . . . . . . . . . 66
vii
5.3 Probabilistic Kripke Structure for CSN Sentiment Change . . . . . 67
5.4 Causal Significance by Year. . . . . . . . . . . . . . . . . . . . . . . 67
6.1 Sentiment Change as a Function of Discussion Topic . . . . . . . . 79
6.2 Causal Significance According to Popular Discussion Topic . . . . . 79
6.3 Probability of Reply Sentiment Conditioned on the Post Sentiment 80
6.4 Dynamic Sentiment as a Function of Classification Threshold θ . . . 80
6.5 Causal significance as a function of the classification threshold θ . . 83
6.6 Probabilistic Kripke Structure for CSN Sentiment Change (Senti-
ment Classifier PPV = 0.84, NPV = 0.68) . . . . . . . . . . . . . . 88
viii
List of Tables
2.1 Summaries of data sets . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 Accuracies (%) of kernel methods with different lengths of walk.
Bold numbers represent best results based on paired t-test (p < 0.05)
on 10-fold cross validation. . . . . . . . . . . . . . . . . . . . . . . . 34
4.1 Symbols & their definitions. . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Summary of Data sets . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3 Classification performance of compared methods, respectively on
five views of Flickr (views’ names were truncated for short). Bold
value indicates the best classifier based on paired t-test (p < 0.01). . 49
4.4 Classification performance of compared methods, respectively on
twelve views of Last.fm (views’ names were truncated for short).
Boldvalueindicatesthebestclassifierbasedonpairedt-test(p < 0.01). 50
4.5 Classification performance of compared methods on multi-view set-
ting for Flickr data set. Bold value indicates the best classifier based
on paired t-test (p < 0.05). . . . . . . . . . . . . . . . . . . . . . . . 51
4.6 Classification performance of compared methods on multi-view set-
ting for Last.fm data set. Bold value indicates the best classifier
based on paired t-test (p < 0.05). . . . . . . . . . . . . . . . . . . . 52
5.1 Features of a Post . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Number of Threads (Thr) vs. Year . . . . . . . . . . . . . . . . . . 66
5.3 Sentiment Change in CSN . . . . . . . . . . . . . . . . . . . . . . . 68
5.4 Yearly Prima Facie Causes of Final Thread Originator’s Sentiment . 72
5.5 Prima Facie Causes of Final Thread Originator’s Sentiment Change 73
5.6 Causal Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.1 Popular Topics in CSN Discussion Threads . . . . . . . . . . . . . . 78
6.2 Prima Facie Causes According to the Classification Threshold θ . . 82
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6.3 Sentiment Change Calculations with Sentiment Classifier PPV = α
and NPV = β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.4 Sentiment Change in CSN (Sentiment Classifier PPV = 0.84, NPV
= 0.68) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.5 Causal Significance Estimates (Sentiment Classifier PPV = 0.84,
NPV = 0.68) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.6 Causal Significance of Sentiment Dynamics when using classification
results of SVM (C ) and Logistic Regression (C ). C0 and C0 are
1 2 1 2
classification results when incorporating PPV and NPV of SVM and
Logistic regression, respectively . . . . . . . . . . . . . . . . . . . . 88
x