Riots and Sociability: a case study of human interaction networks in Qatif Saudi Arabia

82 Pages·2015·10.76 MB·English

"RIOTS AND SOCIABILITY: A CASE STUDY OF HUMAN MASSACHUSETTS INSTIfUTE
OF TECHNOLOGY
INTERACTION NETWORKS IN QATIF, SAUDI ARABIA"
OCT 0L7
BY
MICHAEL ANGELO GRECO III
B.S. COMPUTER SCIENCE, MATHEMATICS, ART LIBRARIES
UNIVERSITY OF WISCONSIN MADISON, 2006
SUBMITTED TO THE DEPARTMENT OF URBAN STUDIES AND PLANNING AND THE
ENGINEERING SYSTEMS DIVISION
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREES OF
MASTER IN CITY PLANNING
AND
MASTER OF SCIENCE IN TECHNOLOGY POLICY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2014
02014 - MASSACHUSETTS INSTITUTE OF TECHNOLOGY ALL RIGHTS RESERVED.
Signature redacted
Signature of Author
Department of Urban Studies and Planning
e ms Division
Te bey 3, 2014
Signature redacted
Certified by
Carlo Ratti
Professor o h actie, Department of Urban Studies and Planning
Thesis Supervisor
Signature redacted
,
Accepted by
Dennis Frenchman
Professor of Urban Design and Planning
Chair, MCP Committee Department of Urban Studies and Planning
Signature
redacted
Accepted by
(
J.
Dava Newman
'
Professor of Aeronautics and Astronautics and Engineering Systems
Director, Technology and Policy Program

"Riots and Sociability: A Case Study of Human Interaction
Networks in Qatif Saudi Arabia"
by
Michael Angelo Greco III
Submitted to the Department of Urban Studies and Planning and the Engineering
Systems Division in partial fulfillment of the requirements for the degrees of Master
in City Planning and Master of Science in Technology Policy
ABSTRACT
Since the onset of the Arab Spring in late 2010, waves of political activism have rever-
berated across much of the Arab world. A growing body of literature has emerged that
explores how new communications and social media technologies have contributed
to, and in certain cases instigated various forms of collective action. However, little
research has examined the effect of these activities on communication patterns them-
selves. This thesis aims to investigate the reorganization of sociability under civil duress
at an aggregate, urban scale.
The study employs a novel approach to communications analysis, applying the Syn-
thetic Control Method to estimate the causal effect of riots on different characteristics
of human interaction within Qatif, Saudi Arabia, after an exogenous shock triggered
a surge in public demonstrations. The analysis reveals a strong, statistically signifi-
cant drop in total call volume, relative to other cities in Saudi Arabia. This is com-
bined with a similarly strong and statistically significant drop in unique daily callers-
demonstrating that people weren't only making fewer calls, fewer people were partic-
ipating in the telecom network each day. Interestingly, daily phone activity is shown
to increase within the subnetwork of users identified to hold strong spatiotemporal
ties to the city, even though their total activity measures (which include connections
both internal and external to the subnetwork) remain constant. This suggests a shift
in callee preference for individuals who are more directly affected by urban unrest.
Lastly, information transmission tests are performed on Qatif's pre and post treatment
interaction networks. Initial research shows that-beyond a 26% diffusion threshold-
information reaches more people faster through the post treatment network. This pro-
vides some support to the hypothesis that communities under duress intelligently reor-
ganize communications to increase dissemination speed and breadth, however, further
research will be required to refine these findings and demonstrate a causal link.
Thesis Supervisor: Carlo Ratti
Title: Professor of the Practice, Department of Urban Studies and Planning
3
Contents
I INTRODUCTION 9
2 DATA AND PROCESSING '5
2.1 Call Detail Records . . . . . . . . . . . . . 15
2.2 Tweets . . . . . . . . . . . . . . . . . . . . 17
2.3 City Selection and Data Aggregation . . . . . 20
2.4 Data Limitations . . . . . . . . . . . . . . . 20
3 METHODS 1
3.' Synthetic Control Methods.. . . . . . . . 21
4 ANALYSIS: CALL BEHAVIOR 25
5 ANALYSIS: INTER AND INTRACITY CALLING PATTERNS 37
5.1 Location Identification . . . . . . . . . . . . 38
5.2 Urban Call Counts . . .. . . . . . . . . . . 39
6 ANALYSIS: TWITTER ACTIVrTY 43
6.1 Geotagged Activity . . .. . . . . . . . . . . . . 43
7 FUTURE DIRECTIONS 48
7.1 Location Estimation for Non-Geotagged Tweets . 48
7.2 Communication Networks . . . . . . . . . . . . 52
7.3 Religiosity . . . . . . . . . . . . . . . . . . . . 56
8 DISCUSSION 6o
4
APPENDICES 64
A APPENDIX: CALL BEHAVIOR 65
B APPENDIX: INTER AND INTRACITY CALLING PATTERNS 69
C APPENDIX: TWITTER ACTvITY 74
81
REFERENCES
5
Listing of figures
i.o.i Protest Images From QatifFollowing Ahmad al-Matar's Death. Found
at: http://khaleejsaihat.com/web3/showthread.php?t=129754 . . . . 14
2.1.1 Geographic Distribution of Cell Towers in Saudi Arabia . . . . . . . 16
2.1.2 Left: Service Type Histogram, Right: Service Detail Description His-
togram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.3 Phone Activity Timeline Over Study Period (Top), Daily Phone Ac-
tivity Timeline of Saudi Arabia, Dec. 12th (Bottom) . . . . . . . . 18
2.2.1 Tweet Timeline Over Study Period (Top), Daily Tweet Timeline of
Saudi Arabia, Dec. 12th (Bottom) . . . . . . . . . . . . . . . . . . 19
4.0.1 Daily call distributions for Dec. 21st and Dec. 28th for All KSA
govornerates (Left), and Qatif (Right) . . . . . . . . . . . . . . . . 26
4.0.2 Trends in total daily network activity, Qatif vs. Other Saudi Gover-
norates, Dec. 20th -Jan. 3rd (Top), and Trends in Average Daily Call
Duration, Qatif vs. Other Saudi Governorates, Dec. 20th - Jan. 3rd
(Bottom). "Treatment" indicated by dashed pink line . . . . . . . . 27
4.0.3 Trends in Total Network Activity, Qatif and Synthetic Qatif (Left),
and Total Network Activity Gap Between Qatif and Synthetic Qatif
(Right) . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 29
4.0.4 Trends in Average Daily Call Duration, Qatif and Synthetic Qatif
(Left), and Average Daily Call Duration Gap Between Qatif and Syn-
thetic Qatif (Right) . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.0.5 Trends in Unique Callers, Qatif and Synthetic Qatif (Left), and Gap
in Unique Callers, Qatif and Synthetic Qatif (Right) . . . . . . . . 32
6
4.0.6 Synthetic Control Placebo Tests with Sabya. Total Daily Network
Activity (Left), Average Call Duration (Middle), and Daily Unique
Callers (Right) ..... .... .. ... ... . .... . .. .. 32
4.0.7 Across-Unit Placebo Tests: Total Activity (all, SOOx or less, roox or
less, Sox or less) . . . . . . . . . . . - - . . . . . . . . . . .. . 34
4.0.8 Across-Unit Placebo Tests: Daily Unique Callers (all, SOOx or less,
oox or less, 5ox or less) . . . . . . . . . . . . . . . . . . . . . . . 35
4.0.9 In-Time Placebo Tests with Qatif. Total Daily Network Activity (Left)
and Average Call Duration (Right) . . . .. . . . . . . . . .. ..36
5.2.1 Trends in standardized intra (top), inter-in (middle), and inter-out
(bottom) call volumes daily network activity, Qatif (solid) vs. other
Saudi governorates (dashed). Dec. 20th - Jan. 3rd. Treatment indi-
cated by dashed pink line . . . . . . . . . . . . . . . . . . . . . . 40
5.2.2 Trends in Intra Call Volumes . . . . . . . . . . . . . . . . . . . . 41
5.2.3 Trends in Inter-In Call Volumes . . . . . . . . . . . . . . . . . . . 41
5.2.4 Trends in Inter-Out Call Volumes . . . . . . . . . . . . . . . . . . 42
6.1.1 Trends in standardized daily Tweet volume (top), Tweet length (mid-
dle), and Tweets per user (bottom), Qatif (solid) vs. other Saudi gov-
ernorates (dashed). Dec. 20th - Jan. 3rd. Treatment indicated by
dashed pink line . . .. . . . . . . . . . . . . . . . . . . . . . . . 44
6.i .2 Trends in Total Tweet Activity, Qatif and Synthetic Qatif . . . . . . 45
6.i .3 Trends in Average Tweet Length, Qatif and Synthetic Qatif . . . . . 45
6.x .4 Trends in Tweets Per User, Qatif and Synthetic Qatif . . . . . . . . 46
7.1.' Trends in Total Tweet Activity, Qatif and Synthetic Qatif . . . . . . 52
7.1.2 Trends in Average Tweet Length, Qatif and Synthetic Qatif . . . . . 53
7..3 Trends in Tweets Per User, Qatif and Synthetic Qatif . . . . . . . . 53
7.2.1 Total Degree Distribution (Left), and Edge Weight Distribution (Right)
of the Complete Reciprocated Network, KSA . . . . . . . . . . . . 54
7.2.2 Fraction of Infected Nodes as Function of Time (Top), Number of
infected Nodes at each instance of t (Middle), and Distributions of
Edge Weights Responsible for Infection . . . . . . . . . . . . . . . 57
7.3.1 Daily Network Activity Distributions from Jeddah (Western Saudi
Arabia), Riyadh (Central Saudi Arabia), and the Eastern Region . . 58
7
7.3.2 Trends in Daily Prayer Time Disruption, All KSA Cities. Qatif drawn
in pink. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
A.o. i Total Network Activity, Qatif and Synthetic Qatif (3 Weeks) . . . . 67
A.o.2 Number of Unique Daily Callers, Qatif and Synthetic Qatif (3 Weeks) 68
B.o.i Intra Call Activity Synthetic Control Placebo Test with Samteh (Left),
In-time Intra Call Activity Placebo with Qatif (Right) . . . . . . . . 70
B.o.2 Daily Local Call Activity . . . . . . . . . . . . . . . . . . . . . . . 71
B.o.3 Across-Unit Placebo Tests: Intra Call Activity (all, 2Ox or less, iox or
less, 5x or less) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
B.o.4 Intra Call Activity, Qatif and Synthetic Qatif (3 Weeks) . . . . . . . 73
C.o. i Tweets Per User Synthetic Control Placebo Test with Ahad Rufaydah
(Left), In-time Tweets Per User Placebo with Qatif (Right) . . . . . 76
C.o.2Across-Unit Placebo Tests: Tweets Per User (all, 50x or less, 2ox or
less, 5x or less) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
8
Introduction
This paper seeks to explore how social unrest affects broad-scale sociability in a city
or region. Since the Arab Spring in the early 2oios, there have been waves of politi-
cal activism across much of the Arab world, including the Kingdom of Saudi Arabia.
A growing body of literature has developed to investigate how new communications
and social media technologies have contributed, and in certain cases instigated vari-
ous forms of collective action. A few studies have considered the impact of mobile
phone access in facilitating collective action, though most have narrowed in on the ef-
fects of a specific emerging technology, like Twitter or Facebook. Furthermore, these
studies follow a wide range of methods that can be broadly grouped into the following
three categories: qualitative approaches that relied on survey data and expert inter-
views; quantitive approaches that characterized the nature of communication patterns
through these new media outlets; or more advanced analytical methods that sought to
isolate the role various communications media played during social unrest. Examples
of these types of research will be discussed in brief over the remainder of this section.
9
Expert interviews are a popular method for subjectively exploring the impact of so-
cial connectivity in urban environments. Tufekci et al. examined the protests in Egypt
by surveying participants in Tahrir Square. They argued that respondents who used
social media were much more likely to attend the demonstrations on the first day.
They further noted that approximately half of those questioned spread media from the
protests online, and that in many cases communication through social media, mobile
phones and face-to-face conversations superseded the role of traditional news media
during the protests. Thus, the authors concluded that social technologies were crit-
ical in diffusing information related to, and encouraging involvement in the activist
intervention Tahir Square [I]. Similarly, Breuer et al. used a qualitative approach to
characterize the role of social media in the Tunisian Revolution. Using a combination
of expert interviews with protest participants and preference survey data from Tunisian
internet users, the authors claimed that new social media forms helped overcome a
government-enforced media blackout by enabling activists to broadcast information
on the movement. Further, they found these collaborative technologies facilitated the
emergence of partnerships between activist groups, and encouraged a kind of 'emo-
tional mobilization' through the depiction of the regime's atrocities in the uncensored
content [2].
Qualitative research has been a popular method for identifying a symbiosis between on-
line communication technologies and social unrest. Another body of research pushed
the relationship between social media and collective action further by quantifying as-
pects of user behavior in response to specific conflict events. Lotan et al compared
and contrasted Tweet broadcasting and news consumption in Tunisia and Egypt [3].
At a high level, this study reiterated the importance of social media as a communica-
tions medium during periods of cultural instability. Furthermore, they built off the
existing state of the literature by highlighting important differences and similarities in
information flow across cultures [31. They found that tweets produced by both in-
dependent activists and mainstream news outlets produced a larger responses in Egypt
than Tunisia. On the hand, Tunisians responded more to news disseminated by blogger
sites, while both countries showed a heavy reliance on information spread by journal-
ists. This research suggests a link between social media platforms and city's cultural
fabric, even though pinpointing this exact relationship eclipsed the scope of the paper.
IO