Social Multimedia Signals: A Signal Processing Approach to Social Network Phenomena

181 Pages·2015·9.18 MB·English

by  Suman Deb Roy, Wenjun Zeng (auth.)

Suman Deb Roy · Wenjun Zeng
Social
Multimedia
Signals
A Signal Processing Approach to Social
Network Phenomena
Social Multimedia Signals
Suman Deb Roy · Wenjun Zeng
Social Multimedia Signals
A Signal Processing Approach
to Social Network Phenomena
1 3
Suman Deb Roy Wenjun Zeng
Betaworks Department of Computer Science
New York, NY University of Missouri
USA Columbia, MO
USA
ISBN 978-3-319-09116-7 ISBN 978-3-319-09117-4 (eBook)
DOI 10.1007/978-3-319-09117-4
Library of Congress Control Number: 2014945344
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Preface
In the 1990s, we noticed that the World Wide Web has had a game-changing
impact on humanity. By the year 2010, we realized that it was the Social Web that
would end up disrupting traditional industries and affecting human physiology
irreversibly. More than a decade into the twenty-first century, we are still scratch-
ing the surface of the enormous data generation engine that is the Social Web. This
book covers three unique ideas in the age of the Social Web. First, there is abun-
dant multimedia added to the Social Web every day. User activity on such multi-
media data generates quantifiable social multimedia signals over time. Second, the
social network governs the behavior and popularity of social multimedia. In fact,
information dissemination in a social network can be expressed as a signal itself.
Finally, this signal may originate in one particular social network domain and then
transfer to other domains on the Social Web, which have semantically similar mul-
timedia data instances. Thus, the online social landscape often behaves as a Ripple
Web, where increased activity on some multimedia data in one domain/platform
generates ripples, which consequently penetrate into other social web domains and
affect behavior of data instances in the latter domains.
Research on social network data has been predominantly focused on the node
and edge linkage (structure) of network. However, there are recent reports that
viral information diffusion in social networks is more strongly influenced by speed
of diffusion than the inherent network structure. Information diffusion occurs due
to activity of social network users. This gives an interesting exposition—if we
can model user behavior on some social multimedia data as a signal, we can use
the rich stash of signal processing methods to study the patterns of this diffusion.
Furthermore, traditional graph processing is computationally more complex than
several signal processing methods. It would be useful to develop signal processing
techniques parallel to graphical methods, empowering the analysis of data behav-
ior in social graphs.
Envisioning a network as a signal generating macro-agent is initially counterin-
tuitive to most. Together, the authors possess more than two decades of experience
in signal processing and multimedia research. When the Social Web and social
media emerged, many multimedia researchers were initially apprehensive to tread
v
vi Preface
the new waters and study this new form of media. One of the reasons could be that
they perceived the social network merely as a graph, and thus labeled it to be in
the domain of network science. Over the past 3–4 years, we have been deliberat-
ing on the best way to get the multimedia research community interested in social
network data analysis.
The biggest motivation for writing this book is to bring the signal processing/
multimedia research community and social network science/sociology commu-
nity together. Signal processing researchers focus a lot on image and video pro-
cessing. There is a plethora of image and video data in social networks which has
been sparsely studied. But more importantly, we propose that the snapshot of a
social network contains a comprehensive signal, very similar to that of an image.
By extension, an evolving network can be imagined to be like a video, where the
network signal changes with time. This could be a great resource for multimedia
researchers, to analyze social multimedia signals for studying media in social net-
works. On the other hand, social network scientists aim to understand properties of
the social network. Sociologists want to explore larger sociological patterns from
network data. Both of them can benetfi by looking at the growth of network data as
a signal. In many situations, it is comparatively less complicated to study the net-
work data behavior as a signal than exploring it through graph theoretic methods.
We have always been very curious about the social web, and how it is funda-
mentally transforming our digital lives. In the course of numerous interactions
with social scientists, sociologists, and multimedia researchers, we have come to
realize that there is an inherent gap between how they perceive the Social Web and
what they want to learn from it. We spotted a niche, a sweet spot between all their
goals—the fact that if they could work with tools built from combined knowledge
their research could be expedited exponentially. Multimedia researchers know a
lot about signal processing, but they know little about what to look for in a social
network. Social scientists know a lot about the phenomena that the social network
could exhibit, but might lack in computational tools that can efficiently extract sig-
nals which could prove or negate their hypothesis.
This book should relate to you if you are curious about the Social Web and
want to develop automated tools to analyze it better. It is especially useful for
researchers who are experienced with signal processing or multimedia analysis but
have little exposure to social networks and social multimedia data. Conversely, if
you are a social scientist, we introduce several signal processing techniques that
you can employ to play with large-scale social data. For those new to signal pro-
cessing, Chaps. 5–7 should get you underway with basic techniques on signal pro-
cessing for social multimedia. On the other hand, if you are new to social media,
the first chapters will be extremely useful to get a thorough look of how social data
behaves. There is also a significant amount of machine learning discussed in later
chapters for those interested in artificial intelligence. Meanwhile, if you are a stu-
dent striving to find a new research topic, there are many absorbing ideas that you
will find within these pages. We wrote this book in a balanced fashion, for multi-
media researchers, social scientists, network scientists, data scientists who work
with social web data, and professionals who use social media on a daily basis.
Preface vii
Chapters 1 and 2 explains the state of the Social Web. Chapters 3 and 4
introduce you to signals, and how networks can generate signals. Following this,
Chaps. 5–7 take an in-depth look into signal processing techniques for social
multimedia data; namely signal detection, signal estimation, and predicting sig-
nal propagation. From Chap. 8 onwards, we start combining the power of social
network data with semantics and signal processing. In Chap. 8, we introduce a
computational engine that allows us to mine social stream data, extract semantics,
and facilitate cross-domain information transfer. This ‘social (information) trans-
fer’ allows development of novel socially aware multimedia applications, which
is discussed in Chap. 9. Many researchers have performed interesting studies on
social network data—which is discussed in Chap. 10. In Chap. 11, we describe
how to leverage the semantic web to gauge the effectiveness of social multimedia
signal processing approaches. Finally, Chap. 12 discusses data visualization—an
effective tool to communicate your research findings with new audiences.
It took us about eight months to conceptualize and bring this book to ink. We
had some strong material from our published research papers. But we also talked
to several researchers in social science, network science, and multimedia commu-
nity, trying to gauge their research aspirations, their frustrations with how material
is often presented in scientific papers and their vision of progress in their respec-
tive fields. We tried to take the best parts of the current research in all the related
fields and complement the issues that frustrate many researchers. Our attempt was
to provide as much relevant information as possible about the current condition
of the Social Web, the automated tools that mine the Social Web, and the areas
that will be extremely promising for future research. Our priorities were three
key phenomena: (1) Social Networks, (2) Signal Processing, (3) Semantic Web. If
you are unsure how these phenomena are interrelated, our book will be a valuable
resource, since it presents each in the light of the other and demonstrates the syn-
ergy between these phenomena.
Finally, when we conceptualized this book, we kept discussing that the defi-
nition of a ‘signal’ needs to be reformed. We felt that a signal essentially con-
stitutes data patterns in time, no matter how the data was being generated. The
IEEE Transactions on Signal Processing acknowledges only audio, video, speech,
image, communication, geophysical, sonar, radar, medical, and music as ‘signals’.
We argued that these sources were not complete. Our argument was that it is not
about the source, but the ‘data’ pattern over time that should be the indispensable
characteristics of what we can call a ‘signal’.
Our proposal was that if a social network generates multimedia, these social
multimedia data patterns would also comprise a ‘signal’. This was not a very
traditional way of thinking in some research communities, even a year back.
How could a social network be or generate a signal? Recently though, the IEEE
Signal Processing society is considering a change in their name (read ‘Power of
a Name’ on the IEEE Signal Processing blog)—to widen its scope, to consider
including ‘data’ as the key term in signal science, and not limit the phenomenon
to just ‘acoustics’ or ‘video’. This should facilitate the multimedia community to
also perceive social networks as signal generating agents.
viii Preface
This book would not have been completed without the encouragement and
assistance of many people. We would like to thank our families and friends for
their support and patience during the entire course of writing this book. We thank
the Department of Computer Science at the University of Missouri. Studies
described in several chapters of this book were performed at their research labs.
Our heartfelt thanks to Microsoft Research, for it is through discussion with
their brilliant researchers that we were able to understand where computer sci-
ence research meets social science and electrical engineering. Dr. Roy would like
to thank Betaworks Studio, where he currently holds the role of a data scientist
working with large-scale Social Web media data. Finally, we want to thank all the
researchers in the Computer Science and Social Sciences, who toiled hours experi-
menting in labs and reported their research through papers and technical reports.
We hope you find your next research topic within the pages of this book.
New York, June 2014 Suman Deb Roy
Wenjun Zeng
Contents
1 Web 2.x .................................................. 1
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2 Media on the Web .......................................... 9
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3 The World of Signals ....................................... 19
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4 The Network and the Signal ................................. 31
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5 Detection: Needle in a Haystack .............................. 43
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6 Estimation: The Empirical Judgment ......................... 59
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7 Following Signal Trajectories ................................ 73
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8 Capturing Cross-Domain Ripples ............................ 95
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9 Socially Aware Media Applications ............................ 115
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10 Revelations from Social Multimedia Data ...................... 135
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11 Socio-Semantic Analysis .................................... 143
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12 Data Visualization: Gazing at Ripples ......................... 161
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Appendix ..................................................... 175