Beauty as a Bridge to NodeXL

Shalin Hai-Jew
Kansas State University
Digital Poster Session,Digital Humanities Forum2015
“Peripheries,Barriers,Hierarchies: RethinkingAccess,
Inclusivity and Infrastructure inGlobal DH Practice”
Sept. 24 – 26, 2015
Institute forDigital Researchinthe Humanities (IDRH),
University of Kansas

 Part1: What is NodeXL Basic?
(Network Overview,Discovery and
Explorationfor Excel/XL)
 Part2: Graphaesthetics
in NodeXL Basic
 Visual pleasure
 Cognitive pleasure
 Part3: Bridging to NodeXL Basic
for research and analysis
2

 Self-intros?
 Researchbackgrounds?
 Areas of interest?
 Experiences withnetwork graphing tools?
 Why “beauty as a bridge”?
 Idea of “bridging nodes” at two ends of a
(weak)bridging edge / link connecting
two otherwise disconnectedclusters in
social networks
 Power of discovery throughbridging and
weak ties
 Beauty as an attention-getter anda
compellingmotivator
3

 A free and open-source network
graphing tool (with a new for-cost
and commercial Pro version with
rudimentary but customizable
sentiment analysis) add-on to Excel
(on Windows OS) available from
Microsoft’s CodePlex (and provided
by the Social Media Research
Foundation)
 A low barrier-to-entry (a free
resource)…but there are actual costs:
 A fairly high learning curve in terms of the
underlying statistics, social network theories,
and logics…and plug-in tool complexity (with
unfixed tool features and limited/no
support); a fairly high learning curve in terms
of social media platforms, policies, and
crowd practices
 Real costs in data collection time, person-
time (data extraction, data processing, data
visualization, and analysis), computational
expense, hardware, software, and computer
system maintenance
 Actual $ costs for the NodeXL Pro version
5

 Acceptsfile imports from UCINET
(full matrix DL file), Pajek,Gephi,
and others [and in the GraphML
graph exchange format];exportsto
UCINET,Pajek,and others
(including GraphMLformat)
 UCINETis a low-cost commercial product
(with enablements for the creationof
synth data andrandomnetworks,among
others,rich large-scale networks,clear
statistical datasets,manually-created
networks,but non-glamorous graphs
IMHO)
 Pajek and Gephi are bothopen-source
and free
 Formerly .NetMap
6

 Its third-party data import feature
enables access to data from a
number of social media platforms
with web-based application
programming interfaces (APIs) or
public web structures: Facebook,
Flickr, MediaWiki, Twitter, Web and
blog (via Virtual Observatory for the
Study of Online Networks or VOSON),
YouTube, and others
 Social media data access requires
various dependencies and varies in
quality
 YouTube extractions have not been
enabled for months now
 Facebook access is limited (if accessible at
all)
 On MediaWiki sites, the user-post
networks feature is not working
 VOSON extractions are human-vetted and
time-delayed; they also must be
education-based or they will not be run on
Australian National University servers
7

 Entities and relationships
 A type of structuralanalysis
 Directed or undirected graphs
 Cyclic (with self-loops) or acyclic
 Single-mode graphs, bipartite graphs
(bigraphs, two-modegraphs),multi-modal
graphs
 Various levels of analysis (differing units of
analysis):
 nodes,isolates,pendants,whiskers;dyads,
triads, motifs;islands; groups / clusters
(communities);global networklevel metrics
 Visual depiction as 2D node-link diagrams
(vertex-edge, entity-relationship),
representativeof selected summary data
 Underlying information-rich datasets (with
both complementary and additional data
to the graph visualizations)
 Continuum fromstatic to highly dynamic
networks (paceof changes over time)
8
SimpleOverview

 “Relationships” (links) on social
media:
 Follower, following (formal, declared); ad hoc
discussions such as reply-to, retweet, likes
/favoriting, commenting, tagging;
cooperative work such as co-tagging, co-
editing; co-liking; upvoting or downvoting on
a particular artifact; shared group
membership; user account profile data;
similarity; shared locale or time zone
 Often weak and transient ties
 One-way relationships vs. reciprocated ones
(or weak reciprocation in return)
 Links also based on messaging and content
(microblogging messages, imagery, audio
files, video files, etc.), metadata (tags,
descriptions, titles, labels), and trace data
(based on actions)
 Technological features of social
media platforms shaping the human
participation and the resulting data
(and vice versa with interaction
effects)
 Different syntaxes: @, #hashtags, keywords,
tags, articles, editors,
 Underlying demographics of respective
participants; human, ‘bot, and cyborg
participation
 External influences and controls on the social
media platforms
9

 Group clustering algorithms:
 Clauset-Newman-Moore
 Wakita-Tsurumi
 Girvan-Newman
 Connected components
 Motif extractions (“motif censuses”), and
others
 Graph layout algorithms: Various
ways to “version” the same
underlying dataset in various
visualizations
 Fruchterman-Reingold force-based layout
algorithm
 Harel-Koren fast multi-scale layout algorithm
 Circle (ring lattice graph)
 Spiral
 Horizontal sine wave
 Vertical sine wave
 Grid
 Polar
 Polar absolute
 Sugiyama
 Random
 None
10

Visual pleasure
Cognitive pleasure
(Part 2)
11

 What is “algorithmicart”?
 Components: Seedingdata (extracted,
synthetic,auto-generated,manually
generated,or other) + data processing+
layout algorithms + layout design=
algorithmic art (in this case basedon
graph visualizations)
 Roles of humans (sometimes); roles of
machines
 What is the hedonic drive? What is
/ are its theorized role(s) in human
lives?
 What make(s) the following graphs
(un)appealing?
 Visually?
 Cognitively?
 Emotionally?
12

Visually
 Symmetry, asymmetry
 Simplicity, complexity
 Color, its presence / absence; hue, saturation,
and brightness; contrast; silhouette
 Line
 Movement
 Relationship
 Figurative representation; abstract
representation
 Dimensionality (1D, 2D, 3D, 4D)
 Patterning
 Illusion
 Mood …
Cognitively
 Elegant algorithmic use; interplay between
underlying data and the software
 Originality, novelty
 Visual illustration of a concept
 Wordplay and messaging (including subtle
messaging, wit, humor, playfulness); meaning
(including hidden steganographic data)
 Backstory
 Serendipity
 Variation
 Mystery
 Strangeness …
140

General Sequence
 Data extraction from social media
platforms (Twitter, Wikipedia, Flickr,
YouTube, Facebook, and others); the
Web (“http networks”) based on
selected parameters; email
networks, and others
 Limits to the numbers of captured nodes
(size of graph)
 Degree network (1, 1.5, or 2; or (1) alters in
an ego neighborhood, (1.5) transitivity, (2)
alters with their respective ego
neighborhoods)
 Type of network
 Types of entities and relationships
 Discrete time period
 Capture of related thumbnail images or not
(from user accounts on various social media
platforms, from related tags on image-
sharing sites, from videos on video-sharing
sites)
 and others…
 Use of seeding (source) term based
on Latin script and UTF-8 character
sets, or use of user name
142

VisualConsiderations in SettingData
Extraction Parameters
 Using data structures as a way to draw
particular graph images / graph maps
 Using large amounts of data as a forcing
functionfor certainalgorithms (a dense
graph)
 Using small amounts of data as a forcing
functionfor certainalgorithms (a sparse
graph)
 Data processing
 Calculationof graphmetrics
 Clusteringalgorithms
 Connectedcomponents (largest)
 Motif extraction(a motif census)
143

 Application of layout algorithm(s)
 Iterating a force-based layout algorithm to
version the graph visualization (with
increasing repulsive / repellence / distancing
force between nodes/vertices with each
iteration)
 Iterating various layout algorithms for
versioning the graph layout (variations of 2D
graph layout based on the layout algorithm)
 Application of visual levers (in various
combinations and sequences):
 Group handling: Box layout algorithms for
groups/clusters
 Graph layout options: margins, intensity of
force-based layout (if used), grid layout,
images at vertices
 Autofill: x- and y-axes, vertex polar R and
vertex polar angle, edges / vertices / groups
represented by data variables (i.e. degrees,
types of centrality, and others)
 Graph pane: Graph options (edges, vertices,
background); dynamic filters, scale, zoom,
and others; visualization processing glitches
144

 More hands-on changes
(applicationof “artifice”)vs.
“organic” aunaturelgraphs:
 Data: Data selectionandfiltering(data
density vs. sparsity); use of synthetic data
fromgraphing tools (like UCINET);
combiningdatasets; settingdata
extractionparameters tolimit andshape
data
 Iterating layout algorithms andeffects:
Strategic sequences of various layouts will
leadto differingeffects
 Illusions: Playingwith 3D illusions (based
onstereoscopicvisionandbright color
advancingand darker colors receding);
motionillusions
 Graph components: Experimentingwith
shapes, textures,line thicknesses; image
rotation,image reversal; maskingportions
of the image
 Interaction effects: Highlightingor
selectingparticular parts of animage to
activate effects (suchas for groups /
clusters,for motifs,for dyadic or other
types of connections)
 Drawing: Manual drawingonthe image,
movingelements of the graph image
145

 More hands-on changes (“artifice”)
(cont.):
 Colors: Tryingout different aspects of
colors [hues,saturation,opacity /
transparency / invisibility, monochrome
(b/w) /dichrome,luminescence on
computer monitors,RGB andhexadecimal
webcolor (including fluorescence),color
filtering, and other features]
 Backgrounding, foregrounding, and
compositing: Addinga backgroundimage
(including with alpha channels and a large
number of other image editing or special
effects); changingup margins
 Glitches (ephemeral): Visualizationof
“deformations”and“glitches” inExcel
(both intended and unintended
capabilities); changingthe aspect ratio;
resizing the window to compress or
expanda visualizationin the graph pane
 Wordplay: Playingthe seeding/ source
terms against the visualization; making
apparent statements; injectingtext into
the visual (on the vertices,onthe edges)
to create visual andmeaning effects
 Others…
146

1. A rudimentary (or greater)
understanding of (social and
content) network analysis theories,
methods [the underlying
calculations and statistics behind
respective measures, data
processing, data visualizations, and
context]
2. Awareness re: the nuances of the
respective social media platforms,
the available content data / trace
data / metadata, and their
application programming interfaces
(APIs) and public-facing sides
(enabling data scraping);
demographics of platform users
 Related tags network, user network, video
network, article network, user-article
network, #hashtag network, keyword search
network, http network, and others
 API rate limiting (and often data limiting);
strategies for continuing data extractions (or
access to the NodeXL server feature by
application)
 Need to apply for “keys” and “secrets” or
have authenticated accounts on the
respective social media platforms to access
some data; to “whitelist”
148

3. Use of NodeXL for practical
research and analysis,
assertability, nuances in research
presentationand qualified
assertions,and some external
data (in)validation
 No “black box“ arrival at conclusions
(without descriptions of how these were
arrivedat)
 No merely invokingthe tool inresearch
but not explainingassumptions,
processes,andmethods
149

Graph Metric Value
Graph Type Directed
Vertices 421
Unique Edges 151
Edges With Duplicates 0
TotalEdges 151
Self-Loops 0
ReciprocatedVertexPairRatio 0.135338346
ReciprocatedEdgeRatio 0.238410596
Connected Components 338
Single-VertexConnected Components 322
Maximum Verticesin a Connected Component 48
Maximum Edgesin a Connected Component 85
Maximum Geodesic Distance(Diameter) 9
AverageGeodesic Distance 3.573008
Graph Density 0.000853976
Modularity Not Applicable
NodeXL Version 1.0.1.323
ReadabilityMetric Value
150

Group VertexColor VertexShape
G1 0, 12, 96 Disk
G2 0, 136, 227 Disk
G3 0, 100, 50 Disk
G4 0, 176, 22 Disk
G5 191, 0, 0 Disk
G6 230, 120, 0 Disk
G7 255, 191, 0 Disk
G8 150, 200, 0 Disk
G9 200, 0, 120 Disk
G10 77, 0, 96 Disk
G11 91, 0, 191 Disk
G12 0, 98, 130 Disk
G13 0, 12, 96 Solid Square
151

Ver t ex Color Shape Size Opacit y I mageFileVisibilit y Label LabelFillColorLabelPosit ionToolt ip Degree I n- DegreeDegree Bet weennessCentralit y ClosenessCentralit yEigenvect orCent ralit yPageRank Clust er ingCoef ficient
ReciprocatedVertexPair
Rat io
AddYourOwnColumns
Her e Followed Follower s Tweet s Favorit es
Tim eZoneUTCOffset
( Seconds) Descript ionLocat ion Web TimeZoneJ oinedTwit t erDat e( UTC)Cust omMenuI temText
Cust om MenuI tem
Act ion Tweet edSearchTerm?
oralia750
ht t p: //abs.t
wimg. com/st icky/def aul
t _prof ile_im
ages/default
_prof ile_5_normal. png
oralia750
RT
@sun__flare:@BarackObam
aYes.Thankyou! !
Pleaset hreat en
VETOSTORMif GOP
doesnot allowvot e
on#RenewUI
#jobs
#RaiseTheWage 0 2 66. 667 0. 006 0. 000 0. 666 0. 000 0. 000 371 226 12913 2004 3/31/201415:14
OpenTwit t er Pagefor
ThisPer son
ht t ps: //twit t er.com/orali
a750 Yes
barackobama
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/451007105391022080
/iu1f 7brY_normal. png
barackobama
14 0 1264. 995 0. 008 0. 000 3. 555 0. 055 0. 000 651012 42869915 11704 4 - 14400
Thisaccountis
runby
Organizingf or
Act ionst aff .Tweet sf romt he
President aresigned- bo.Washingt on,DC
ht t p: //t .co/
O5Woad92z1
East er n
Time( US&Canada) 3/5/200722: 08
ThisPer son
ht t ps: //twit t er.com/bara
ckobam a No
a_new_f reedo
m
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/45526317
9048636416
/ZGhBhuFZ_normal. png
a_new_f reedo
m
@Sincer eAngelsI knowyou
arest ill
f ight ing, had
not seenyou
m uch.
I t ryt ost ayin
t ouchwit h
ever yone.
#RenewUI
#vet s#jobs#immigration 3 0 63. 333 0. 005 0. 000 1. 060 0. 000 0. 000 1985 1568 22322 1970 - 25200
I nt er vent ions,
Counseling,
Consult ing,
Af t er careand
Peer t oPeer
Counseling. Web
sit e
ht t p: //t .co/TEWRv8NdUsPhone
541- 513-4805
Facebook
RichardThayerPort land, Oregon
ht t p: //t .co/
Tnf zk2jvyR
Pacif icTime
( US&Canada) 6/8/20119:13
ThisPer son
ht t ps: //twit t er.com/a_ne
w_f reedom Yes
_denver jobs_
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/45352265
9339296768
/zThRE85b_normal. jpeg
_denver jobs_
#Denver #J obs
Taver nishiring
anexper ienced
Manager:The
Taver n
Hospit alit y
Groupis
seekingan
exper ienced. ..
ht t p: //t .co/TJw4f hbUVv 0 2 2. 000 0. 500 0. 000 1. 459 0. 000 0. 000 925 206 8376 0 10800
#J obs#Of fersin
#Denver ! !Denver , CO At hens 4/8/201413: 18
ThisPer son
ht t ps: //twit t er.com/_den
ver jobs_ Yes
denver jobsfair
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/2147872863/Bground
888dabA_normal. jpg
denver jobsfair
ht t p: //t .co/s74
Zim 0uOoCler ical
- At t endance
Douglas
Count ySchool
Dist rict -
HighlandsRanch, CO
#Denver #CO#J obs 1 0 0. 000 0. 333 0. 000 0. 770 0. 000 0. 000 9 113 13098 0 - 10800
SearchDenver/
Healt hcarejobscareer list ing.
Denver ,
Colorado
ht t p: //t .co/
OcwDvYMBJ Q
At lant ic
Tim e( Canada) 8/14/201123:05
ThisPer son
ht t ps: //twit t er.com/denv
er jobsf air Yes
aurorajobs_
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/45168401
0985398273
/uFwhorWB
_normal. jpe
g
aurorajobs_
#Aurora#J obs
J avaWebApplicat ion
Developer /Tro
ubleshoot er:
Greenwood
VillageOur
client is
seekinga
javawe. . .
ht t p: //t .co/YYt
5ZL3Yvx 1 0 0. 000 0. 333 0. 000 0. 770 0. 000 0. 000 971 67 3944 0 10800
#J obs#Of fersin
#Aurora! !Aurora, CO At hens 4/3/201411: 32
ThisPer son
ht t ps: //twit t er.com/auro
rajobs_ Yes
jobsdirect usa
ht t p: //pbs.t
wimg. com/prof ile_image
s/18077445
42/jdusat wi
t t er _normal
. png
jobsdirect usa
#publishing
#jobsAdminist rativ
e
Assist ant II
Publishing
Sales
- J . J . Keller
&am p;
Associat es,
I nc- Neenah,
WIht t p: //t .co/7B
TlpFMHE4 1 11 798. 167 0. 006 0. 000 4. 958 0. 000 0. 091 19587 19345 673670 93 - 14400
HelpingAmer ica
get backt o
worksince2008.
Findjobsint he
Unit edSt at es
andsearch
companycareer
sit esatht t p: //t .co/egDN
Xrgi4Z. J acksonville,FL
ht t p: //t .co/
uzt KKVZ7L0
East er n
Time( US&
Canada) 2/18/200922:23
ThisPer son
ht t ps: //twit t er.com/jobs
direct usa Yes
mnchester_bu
zz
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/3169827508/b8f 76348
c4bf 81a460a
1ba87d7e3d
76d_normal.
jpeg
mnchester_bu
zz
Execut ive
Administ rativ
eAssist ant
at Career
Connect ions( Manchester,
NH)
ht t p: //t .co/Xn
6crPSwWz
#J obs 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 2934 3218 48508 2 - 25200
ht t p: //t .co/B8Q0
wvkV ManchesterNH
ht t ps: //t. co
/t lHOkKI Y
Pacif icTime
( US&
Canada) 4/3/201015: 27
ThisPer son
ht t ps: //twit t er.com/mnc
hest er _buzz Yes
lijobs_mkt g
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/11641219
19/linkedinl
ogo_normal.png
lijobs_mkt g
VPMarket ing
- BigDat a
St art Up
at Concurrent
I nc. - San
FranciscoBayArea#in
#jobs
ht t p: //t .co/oB
eRNhQovJ 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 1 9074 71060 0
Market ingjobsont heworld's
largest professionalnetwork.ht t p: //t .co/2yk13jJ tDw9/23/201022:13
ThisPer son
ht t ps: //twit t er.com/lijob
s_m kt g Yes
west corp_oma
ha
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/461291542058000385
/nBN-
xxMv_norm
al. jpeg
west corp_oma
ha
OrderManagement
Administ ratorjobat West
Corporat ion
Om aha
ht t p: //t .co/O6
YPMd4qP1
#I ndeed
#jobs 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 216 360 1825 23 - 18000
West ’ sgoalist o
seam lessly
connect our
client st ot heircust omersby
deliver ing
world- class
solut ionst hatmaket hebig
dat acraze,
well…. not so
biganymore.Om aha, NE
ht t p: //t .co/
7Ypq9ASTK
R
Cent ral
Time( US&
Canada) 2/22/201017:07
ThisPer son
ht t ps: //twit t er.com/west
corp_omaha Yes
kccareers
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/21465026
22/KC_Bug_
normal. png
kccareers
J ob: Epit helial
Biologist -
Kimber ly- Clark
- Neenah, WIht t p: //t .co/bd
hjGuvt dT
#jobs 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 53 1579 3538 0 - 18000
Leadingglobal
healt handhygiene
com pany.
Brandsinclude
Kleenex, Scot t,
Huggies, Andrex,
PullUps, Kot ex,
Poise, and
Depend.
Acrosst he
count ry
ht t p: //t .co/
CZBX6ji01O
Cent ral
Time( US&
Canada) 6/17/200917:36
ThisPer son
ht t ps: //twit t er.com/kcca
reer s Yes
newjobsnow
ht t p: //pbs.t
s/21551150
11/njn1_nor
m al. jpg
newjobsnow
Collect ions.
Nort hCant on,
OH.
ht t p: //t .co/Qn
KI ekLz4Y#jobs 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 2354 2902 184116 0 - 14400
Amer icawasnot
built onf ear.
Amer icawas
built oncourage,onimaginat ion
andan
unbeat able
det er minat ion
t odot hejobat
hand. ~HarryS.
Trum an Unit edSt at esofAmerica
East er n
Time( US&
Canada) 4/22/20123: 19
ThisPer son
ht t ps: //twit t er.com/newj
obsnow Yes
lijobs_eng
ht t p: //pbs.t
s/11641198
57/linkedinl
ogo_normal.
png
lijobs_eng
Senior
Engineer /MTS
at Alt er a-
SanFranciscoBayArea#in
#jobs
ht t p: //t .co/pA
Eg8kuhFX 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 1 3841 90149 0
Engineer ingjobsont heworld's
largest professionalnetwork.ht t p: //t .co/GnqTC9qBJI9/23/201022:11
ThisPer son
ht t ps: //twit t er.com/lijob
s_eng Yes
wellpoint care
er
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/45649887
8796165120
/ldBdt SbF_normal. png
wellpoint care
er
Manager II
Provider
Net workManagement
…jobat
WellPoint -
Knoxville
ht t p: //t .co/wk
EMvwhyPX#I ndeed
#jobs 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 64 397 2659 0 - 14400
Lookingf or
great t alentf or
ourcompany,
ref er ralsare
always
welcomed.RT
arenot
endorsement s.
Forcust omer
ser vicet weet@askAnt hem.I ndianapolis,I N
ht t p: //t .co/
z6J vGyy8vE
East er n
Time( US&Canada) 9/30/20101: 56
ThisPer son
ht t ps: //twit t er.com/well
point career Yes
t eamworkonli
ne
ht t p: //pbs.t
s/34938015
9/t wol_nor
m al. jpg
t eamworkonli
ne
Newest #J obs
Post ed: Ticket
Accout ing
Specialist -
ArizonaDiamondbacks
( Phoenix, AZ)
ht t p: //t .co/ZN
69RouMSb 1 0 0. 000 0. 005 0. 000 0. 533 0. 000 0. 000 499 25686 39701 125 - 14400
TeamWorkOnlin
eist he#1
dest inat ionfor
#sport sjobs.
Applyf or
#sport sbiz#jobswit hourclient s
int he#MLB,
#NHL, #NFL,
#NBAandmore!
Shaker Heights,
OH
ht t p: //t .co/
ND35VSp3
nP
East ern
Time( US&
Canada) 8/5/200913: 42
ThisPer son
ht t ps: //twit t er.com/t ea
m workonline Yes
alexandrahebe
r
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/31868639
03/bdd6f c5f
37b6e5b425
70f f b04071b
d28_normal.
jpeg
alexandrahebe
r
HUGEBEAT:
AUSTRALI AN
ECONOMY
ADDED
14, 200#J OBS
I NAPRI L
ht t p: //t .co/I6T
f onpK3h 0 4 41. 643 0. 006 0. 000 1. 006 0. 250 0. 000 641 446 1388 43 36000
Abusiness
journalist . Can
cook. Northern
Beacheskidat
heart . Aust ralia
ht t p: //t .co/
AlwX3oKXi
C Sydney 7/8/20115:20
ThisPer son
ht t ps: //twit t er.com/alex
andraheber Yes
abcnews24
ht t p: //pbs.t
s/14630585
79/24_Twit t
er _Avat ar_
normal. jpg
abcnews24
#J obsf igures
f orAprilrem ained
st ableat 5. 8%
wit hjust over
14, 000
jobscreat ed 6 2 147. 243 0. 006 0. 000 1. 941 0. 179 0. 000 1383 114376 33249 8 36000
Thisaccount
det ails
#ABCNews24’s
livecover age,
guest sand
programs.Alsof ollow
@ABCNewsf orgener alnews
aler t s. Thisisan
of f icial
@ABCaustralia
account . Aust ralia
ht t p: //t .co/
9KbXOReBI
b Sydney 4/5/20102:26
ThisPer son
ht t ps: //twit t er.com/abcn
ews24 Yes
com m sec
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/18324508
36/avat ar_n
ormal. jpg
com m sec
14, 200jobs
creat edin
April( all
f ullt ime) . No
changet opart
t im eemployment.
J oblessrate
st eady
at 5. 8%.
#ausecon
#jobs^SD 2 3 12. 800 0. 006 0. 000 1. 003 0. 250 0. 250 469 12345 19611 3 36000
Keepupt odate
wit ht helat est
newsand
inf ormationon
invest m ent
market sf rom
Aust ralia's
leadingonlinebroker Aust ralia
ht t p: //t .co/
PwTcRZF6iZ Sydney 11/3/200811:48
ThisPer son
ht t ps: //twit t er.com/com
m sec Yes
guardian
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/2814613165/f 3c9e398
9acac29769c
e01b920f 52
6bb_normal.
png
guardian
4 0 2. 667 0. 005 0. 000 1. 040 0. 250 0. 000 1074 2179693 61815 130 3600
Topst ories,
specialf eat ures,liveblogsand
m oref romht t p: //t .co/rrGq
778cPt London
ht t p: //t .co/
rrGq778cPtLondon 11/5/200923:49
ThisPer son
ht t ps: //twit t er.com/guar
dian No
ranger syariah
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/420006782698655744
/-kbZkaFe_no
rmal. jpeg
ranger syariah
RT
@J obs_secretary: AU
#admin#jobsExpressionsof
I nt erest-
Mining,
Oil andGas
I ndust ries:
QLD- Brisbane,
Workf orthe
best and. ..
ht t p: … 0 1 0. 000 0. 029 0. 009 0. 349 0. 000 0. 000 50 129 1817 0 25200
ingin
menyelamatkan
duniadari
godaanset an
yangt er kutukMARKAS008 Bangkok 1/6/20141:37
ThisPer son
ht t ps: //twit t er.com/rang
er syariah Yes
jobs_secret ary
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/42911910
2083280896
/dkC4c5kw_normal. jpeg
jobs_secret ary
AU#adm in
#jobs
Expressionsof
I nt er est-Mining, Oil
andGas
I ndust ries:
QLD- Brisbane,
Work
f ort hebest
and. . .
ht t p: //t .co/SvMb5cf gM9 4 0 24. 000 0. 045 0. 061 0. 939 0. 500 0. 000 31 364 7954 0 36000
Findt hejobf or
youon
ht t p: //t .co/VC1k
yVMgK9.Subscribet ot his
f eedf orAdminist ration
&Secret arial
jobsast hey're
list ed. Aust ralia
ht t p: //t .co/
Xrhf Nt Ff49Sydney 10/4/20110: 55
ThisPer son
_secret ary Yes
mnjobconnect
er
ht t p: //pbs.t
s/37880000
0648413438
/b06e6f 2eb7
a22adea096
f 2a8c066288
6_normal.pn
g
mnjobconnect
er
#jobs|Macy'sRosedale
Cent er,Roseville, MN:
DeliSales
Associat e,
Part Timeat
Macys
( Minnesota):Ove. . =. ..
ht t p: //t .co/Ike
Lm Qabz9 0 1 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 120 137 6882 0 - 25200
TheLonestar
St at e:
ht t ps: //t. co/hoL7
aDT8Yt :
@USAJ obConnec
t er Minnesot a/MN
ht t p: //t .co/
Qof LHunRpcArizona 12/10/20121:41
ThisPer son
ht t ps: //twit t er.com/mnjo
bconnect er Yes
jobsat seek
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/3192840101/7810c66
93482c82f1eb0430967db
292d_normal. jpeg
jobsat seek
Blast er - 2nd
shif t jobat
SEEK
Career s/Staff i
ng, I …-
Baldwin
ht t p: //t .co/ro
ULi5Rq0w
#I ndeed
#jobs 1 0 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 1620 1318 7784 122 - 18000
SEEK
Career s/Staff ing:locallyowned,
43yrsRecruit ing
Wisconsin
Minnesot aJ obs,
Hiring
Tem poraryDirect Hire
Manuf act uringOf f ice
ht t ps: //t. co/uNIOf 8I 4Tn
Wisconsinand
Minnesot a
ht t p: //t .co/
q29Vj0ZlFs
Cent ral
Time( US&Canada) 10/18/201016: 14
ThisPer son
at seek Yes
f indm job
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/21250132
20/Prof ile_n
ormal. png
f indm job
SeniorWeb
Developerht t p: //t .co/jDk
U28yGI d
#wordpress
#javascript
#jobs#hiring
#career s 0 1 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 1735 2980 261320 0 28800
ht t p: //t .co/zpTOXPV0pl#jobs
#carrer s
ht t p: //t .co/
zpTOXPV0p
l Beijing 4/9/201213: 27
ThisPer son
ht t ps: //twit t er.com/f ind
m job Yes
t hwjobs
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/2625201241/qhz8rc0u
5rvdi8rieh8
0_normal.jp
eg
t hwjobs
RT
@PeopleFluen
t J ob: New
#job:
Senior
Consult ant
Locat ion:
Raleigh. .
ht t p: //t .co/tq9
t TOosUb#jobs
#hiring 1 0 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 3549 3779 76567 7 - 10800
Theleader in
#cloud#er p#HRI S#HRMS
#HCM#HRt ech#PM#jobs: our
jobs#t hwjobs:
RECRUI TERSwe
aut oRT:m obile?
ht t p: //t .co/ZrCr8kU5TH USA
ht t p: //t .co/
Pf jly5QMvD Brasilia 11/3/201120:52
ThisPer son
ht t ps: //twit t er.com/t hwj
obs Yes
railsjobscom
ht t p: //pbs.t
s/3388505552/8937478
ecca1f a7352f 3ad41cff e5c
54_normal. p
ng
railsjobscom
NewJ ob
Aler t : SrRubyonRails
Developer -Unit edHealt h
Group- Broomfield,
CO: RubyonR. . .
ht t p: //t .co/gRS2PuOUim
#jobs#r uby
#r ails 0 1 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 5 161 3264 0 - 14400
Theleadingjobboardf orRails
developer s.
ht t p: //t .co/
6qklmQ5M
Cf
East er n
Time( US&
Canada) 3/11/201319:21
ThisPer son
ht t ps: //twit t er.com/railsj
obscom Yes
rorjobs
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/12082360
6/t wit t er log
o_normal.gif
rorjobs
NewJ ob
Aler t : SrRuby
onRails
Developer at
Unit edHealt h
Group
( Broomfield,
CO) : Det ails. ..
ht t p: //t .co/bC
hMjiRrc1
#r ails#r uby
#jobs 1 0 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 40 2489 6115 0 - 14400
RubyonRails
J obsbyRubyon
Railsdeveloper s
f orRubyonRailsdeveloper s.NewYork
ht t p: //t .co/
bnT2P2jwXn
East er n
Time( US&Canada) 4/3/200916: 09
ThisPer son
ht t ps: //twit t er.com/rorjo
bs Yes
velvet jobs
ht t p: //pbs.t
s/42096767
6815482880
/nuY2OP0A_
normal. jpeg
velvet jobs
AFinance
Manager -
Proper t yPLC
isavailablewit ha
FinancialSer vices
Firm! Checkitout here:
ht t ps: //t. co/d
UNgAH7uTm
#f inance#jobs 0 2 18. 800 0. 006 0. 000 0. 586 0. 000 0. 000 1353 1193 3484 287
Velvet J obs®-
TheUlt imat eMat chmaker sin
Fashion&Beaut y,
Ent er t ainment&
Media, Finance
&I nvest mentLosAngeles,CAht t p: //t .co/9PnekR1fSU7/29/201318:42
ThisPer son
ht t ps: //twit t er.com/velv
et jobs Yes
cnbc
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/46266680
1009020928
/6kJ aGlK5_n
ormal. png
cnbc
5 0 103. 405 0. 006 0. 000 1. 293 0. 100 0. 000 847 1460662 34551 1674 - 14400First inBusinessWorldwide
ht t p: //t .co/
YKho1OnI v
S
East er n
Time( US&
Canada) 2/9/20090:03
ThisPer son ht t ps: //twit t er.com/cnbcNo
newarkjobroll
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/37880000
0554519306
/c7c701485d6e639e61b1
05b7038660
23_normal. p
ng
newarkjobroll
#NYC#jobs|
Regist eredNurse( RN)
at TakeCareHealt h
Syst em s
( Newark,
NJ ) : Newark,
NJ Wehave
ano. .
=. . .
ht t p: //t .co/n8L
cxMRzBN 1 1 0. 000 1. 000 0. 000 1. 000 0. 000 1. 000 87 73 4142 0 - 25200
m et rocit ies
@NYCMetroJ ob
Roll: 50st at es
@USAJ obConnec
t er List s Newark, NJ
ht t p: //t .co/
f 1YQzPgGAl
Pacif icTime
( US&
Canada) 2/22/201312:40
ThisPer son
ht t ps: //twit t er.com/new
arkjobroll Yes
brooklynjobrol
l
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/37880000
0554444376
/edee5a0f92
6e0a8c2299
274f b1a2fff c_normal. png
brooklynjobrol
l
#NYC#jobs|
Occupat ional
Ther apist
Assist ant :
5/13and5/16
t hrough
5/30, hours
830am- 430pm
at Mi. .
=. . .
ht t p: //t .co/FEl9FbHTrv 1 1 0. 000 1. 000 0. 000 1. 000 0. 000 1. 000 44 103 4478 0 - 25200
m et rocit ies
@NYCMetroJ ob
Roll: 50st at es
@USAJ obConnect er List s Brooklyn, NY
ht t p: //t .co/
EOmJ oKnTb4 Arizona 2/22/201312:14
ThisPer son
ht t ps: //twit t er.com/broo
klynjobroll Yes
nmjobconnect
er
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/37880000
0692624425
/19d8f 84e94dcbd0f 2b89e
97de5d0356
c_normal. pn
g
nmjobconnect
er
#jobs|G
Unloader
Processorat
Walm art
Milit ary( Los
Lunas, NM) :
G
Unloader Proc
essorJ obLocat ion:
Los. . =.. .
ht t p: //t .co/L99
AkeQVlj 0 1 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 12 37 2506 0 - 25200
The
Enchant mentSt at e|
#NMJ obs|Like
usonFacebook
ht t p: //t .co/23jB
UhhuW1|St at e
cit iesont he
List s|50st ates
@USAJ obConnec
t er List s
NewMexico/
NM
ht t p: //t .co/
wRja1UeRu
0
Pacif icTime
( US&
Canada) 2/19/201319:06
ThisPer son
ht t ps: //twit t er.com/nmjo
bconnect er Yes
abqjobroll
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/37880000
0838508636
/162355453
8a8f 9c8b12e
aed016f ccfb8_normal.jp
eg
abqjobroll
#ABQ#jobs|
I nf ormationProf essional
at U. S. Navy
Reser ve
( Albuquer que,
NM) :
I nf ormation
Prof essionals..
=. . .ht t p: //t .co/w5
oBgaw9mp 1 0 0. 000 1. 000 0. 000 1. 000 0. 000 0. 000 16 35 3806 0 - 25200
Likeusat
ht t p: //t .co/qI fwY
V39oN|
St at ewidecit ies@NMJ obConnec
t er list s|50st at es
@USAJ obConnec
t er list s
Albuquer que,
NM/ABQ
ht t p: //t .co/
p75crKI KmIArizona 9/28/201317:04
ThisPer son
ht t ps: //twit t er.com/abqj
obroll Yes
ht t p: //pbs.t
wimg. com/p
rof ile_image
s/45409770
4545579008
/kcSPnvSM_
rober t lopez_
RT
@ChelseaKros
t : #Millennials
MUSTuse#LinkedI n-
Saysourguest s
t onight@kimber leyka
sper
@Nast yGalCar
eer s
ht t p: //t .co/wU
KpoXaSRd
Christ ian
; D♥! ( 8) /Superman_For
Christ opherReeve♥
WindowsLiverober t olopez144
000@live. com
FBCadreRobert
Google+
rober t olopez144
Wordpress
ht t ps: //t. co
/HhwN2wo
Pacif icTime
( US& OpenTwit t erPageforht t ps: //twit t er.com/robe
Ve rte x 1 Ve rte x 2
oralia7 5 0 barackobama
oralia7 5 0
a_ ne w_freedo
m
_ de nverjobs_denverjobsfair
_ de nverjobs_aurorajobs_
jobsdirectusa
mnchester_bu
zz
jobsdirectusalijobs_ mktg
jobsdirectusa
we stcorp_oma
ha
jobsdirectusakccare e rs
jobsdirectusane wjobsnow
jobsdirectusalijobs_ eng
jobsdirectusa
we llpointcaree
r
jobsdirectusa
te amworkonlin
e
ale xandrahebe
r abcne ws2 4
ale xandrahebe
r barackobama
ale xandrahebe
r commse c
ale xandrahebe
r guardian
range rsyariahjobs_secretary
mnjobconnect
e r jobsatse e k
findmjob thwjobs
railsjobscomrorjobs
ve lve tjobs barackobama
ve lve tjobs cnbc
ne warkjobroll brooklynjobroll
brooklynjobrollne warkjobroll
nmjobconnect
e r abqjobroll
robe rtlopez_barackobama
robe rtlopez_che lse akrost
jobs4 e p jobs4 hou
jobs4 e p jobs4 dal
jobs4 e p jobs4 sa
jobsinboston_bostonjobssh
jobsinboston_jobs4 bos
trave lworld37
3 craigslistjobs
jobsdirectusabe stjobsonline
trave lworld37
3 be stjobsonline
trave lworld37
3 jobs4 nyc
commse c abcne ws2 4
commse c mattcnbc
commse c cnbc
mattcnbc commse c
mattcnbc cnbc
mattcnbc abcne ws2 4
mattcnbc barackobama
wildlifecenter e coe mploy
e ve ryinternshi
p jobs4 nyc
e ve ryinternshi
p barackobama
_ florida_jobs_jobtipsco
se ipai2 3
a_ ne w_freedo
m
se ipai2 3 the hill
ve ronicamac2
1 jobs4 rdu
sun_ _ flare
a_ ne w_freedo
m
sun_ _ flare matt2 8 1 9 70
sun_ _ flare the hill
sun_ _ flare barackobama
re cruiternatha
n sapjobssydney
joe e lector the hill
joe e lector ricksantorum
nyc_ it_ jobs jobs4 nyc
jobs4 me sa jobs4 lv
jobs4 lv jobs4 sd
jobs4 lv jobs4 phx
jobs4 lv jobs4 me sa
jobs4 jan jobs4 me m
sacbiz cnbc
age ntse ntral cnbc
nie ve ra myne gosentro
myne gosentrobnie ve ra
myne gosentronie ve ra
myne gosentroage ntse ntral
myne gosentro
dominicpantoj
a
age ntse ntral myne gosentro
dominicpantoj
a myne gosentro
bnie ve ra myne gosentro
nie ve ra age ntse ntral
nie ve ra bnie ve ra
nie ve ra
dominicpantoj
a
age ntse ntral nie ve ra
dominicpantoj
a nie ve ra
bnie ve ra nie ve ra
age ntse ntral bnie ve ra
age ntse ntral barackobama
age ntse ntral
dominicpantoj
a
dominicpantoj
a age ntse ntral
bnie ve ra age ntse ntral
dominicpantoj
152

Top URLs in Tweet in EntireGraph EntireGraph
Count
Top Domains in Tweet in EntireGraph EntireGraph
Count
Top Hashtagsin Tweet in EntireGraph EntireGraph
Count
Top Words in Tweet in EntireGraph EntireGraph
Count
Top Word Pairs in Tweet in EntireGraph EntireGraph
Count
Top Replied-To in EntireGraph EntireGraph Count
Top Mentioned in EntireGraph EntireGraph Count
Top Tweetersin EntireGraph EntireGraph Count
craigslistjobs 1381070
bestjobsonline 1155301
zrjobs 738029
jobsdirectusa 673670
creativejobfeed577897
jobangebote 441713
findmjob 261320
robertlopez_ 259977
bestjobwebsite 209404
mostpopularjobs 192311
153

 Edgefilters: relationship date,
communicationsdates,in UTC or
coordinateduniversal time
 Vertexfilters: x-axis,y-axis, in-
degree, out-degree,betweenness
centrality,closeness centrality,
eigenvectorcentrality, PageRank,
clusteringcoefficient,reciprocated
vertex pair ratio,followed,
followers,Tweets (if relevant),
favorites,Time Zone UTC offset
(seconds),Joined Twitter Date
(UTC) (if relevant),and others
154

datasheets<-> social network graph in graph pane (interactivityby highlighting for
cross-referencing) 155

Alphanumeric String Labeling
 Title
 Labels of vertices and lines
 Labels of groups
 Lead-in and lead-away texts; linked data;
linked datasets
Vertex andLine Representations
 Vertex thumbnail imagery or glyphs
 Sizes of vertices (nodes) and thickness of
edges (links or lines)
 Arrows for directionality
Spatial Positions onTwo Dimensions
 Positions of vertices and lines
 Placement on the x- and y-axes
(sometimes)
 Partitioning of groups and clusters
Color
 Color to indicate grouping or clustering
 Color intensity to indicate frequency
Graph MapLegendor Key (outside graph
itself)
 Textual description of the graph and
symbology
156

“threshold”related tagsnetwork on Flickr (1.5deg.)
157

158
“Internet”relatedtagsnetwork on Flickr (1.5deg.)

“network”related tags network on Flickr (2 deg.) / with group or clusterpartitioning
159

“Yangtze”related tags network on Flickr (1 deg.) 160

161
“night” related tagsnetwork on Flickr (1.5deg.)

162
“flicker” related tags network on Flickr (1.5 deg.) / group partitioning

163
“tomato” relatedtagsnetwork on Flickr (1.5deg.) / manual group labeling

“Greece”related tagsnetworkon Flickr (1.5deg.)
164

165
“weekend” video network on YouTube (unlimited)

166
“air quality”video network on YouTube (unlimited)

“quantumcomputing”video network on YouTube (unlimited)
167

“bridge”video network on YouTube (unlimited)
168

@johnkerry ad hoc user network on Twitter based on recent messaging
169

170
@AlexSalmonduser ego neighborhood network on Twitter (1 deg.)

171
#elicitation hashtagsearch on Twitter (basic network) / one conversation

172
#sentiment hashtagsearch on Twitter (based network) / built-in sentiment
analysis based on a lexicon dictionary in NodeXL Pro

173
#ebola hashtagsearch on Twitter (basic network) / top 10 group keywords

#rivalry hashtagsearch on Twitter (basic ad hoc network) / who’s talking
174

175
#Sweden hashtagsearchon Twitter (basic network) / 4,854vertices 0 edges / people
talking to themselves in online public spaces?

176
@wacomuser network on Twitter (2 deg., 10-personslimit)

“Hersheys”fan page on Facebook (dataeven without node-level labeling)
177

#virscan hashtagsearch on Twitter (basic network)
178

179
#alert hashtagsearch on Twitter (basic network) / collapsed groups

180
#NSA hashtagsearch on Twitter (basic network) / keywords in top 10 groups

181
#draft hashtagsearchon Twitter (basic network) / multidimensional data

182
#avian #flu hashtagsearch on Twitter (basic network) / Boolean search data

“ambchrishill” ad hoc user network on Twitter including friends (formal
declared / verified network)
183

184
#share hashtagsearchnetwork on Twitter(1 deg.)

#Everest hashtagsearchon Twitter (basic network), with key terms in top 10
groupshighlighted
185

186
#14dayshashtagsearch on Twitter (basic network)

“Alexis_Tsipras”article network on Wikipedia (1 deg.) / about the individual
187

188“Chinese_yuan” article network on Wikipedia (1 deg.) / topic-based

189“Malaysia_Airlines_Flight_370” articlenetwork on Wikipedia (1 deg.) / event-based

190
“2015_Nepal_earthquake”articlenetwork on Wikipedia (1 deg.) / event-based

191
“User:Ahunt”articlenetwork on Wikipedia (1 deg.) / human editor network

192“Wikipedia” article network on Wikipedia (1 deg.) / about Wikipedia

193“User:BG19bot”article network on Wikipedia (1 deg.) / flagged bot ego neighborhood

194
syntheticdata for a “privilege walk” / inherited lifestyle features and proximity to
power at core

 Empirical data extractions from “big
data” datasets (but N ≠ all); ideally
continuous and full data scrapes to
databases for large-scale questions
 Rate-limited APIs; data-limited APIs; tool-
limited data extractions
 Incomplete (and likely non-random sample)
of a population
 Slice-in-time discrete-temporal cross-
sectional data (or paired data over time);
continuous data within a particular time
period (server version of NodeXL)
 Restricted to 1 deg., 1.5 deg., and 2 deg.,
without higher order connectivity (2.5 deg.,
3…up to 6+ degrees of separation)
 Mixed sourcing ecosystem
 Human, ‘bot, and cyborg entities
 Human social performance and “fronting,”
deceptions, exaggerations; various
(sometimes masked) intentions
 Limited “line of sight” from individuals
 Mix of often-unsourced primary and
secondary information from social media
 Crowd-vetted data but often with suspension
of disbelief (fast trust) and sub-second
decision-making (fast-sharing with what is
top-of-mind; speed to outrage and judgment,
slander, and libel; “tempest in a teacup”
phenomenon)
195

 Detracting “noisy” (vs. signal-rich)
multimedia data types (for content
analysis):
 Succinct microblogging messaging (e.g. 140
characters)/SMS practices, brief comments
and reviews; shorthand
 Decontextualized messaging, polysemic word
senses (and required disambiguation),
misspellings, and others
 Changing slang, idioms, jargon, emoticons,
and symbology
 Fast-evolving global and local cultural
practices
 Multimodal and multimedia digital artifacts
with inherent ambiguity (images, audio,
video, multimedia, games, simulations, web
pages, and others)
 Multilingual global environment
 Challenges with research
requirement for both of the
following:
1. data (in)validation through multiple
collection and analytical methods, and
2. calculation of confidence measures and
error rates to collected sets and analyses
196

 “Hairball” graph-based data
visualizations
 High data density (vs. sparsity)
 Overwhelming uses of color, form, lines,
and details (for human visual processing,
based on Gestalt principles)
 Complex rules for accurate network analysis
 Difficulty of graphical data parsing;
requirement to go to the underlying data for
actual meaning
 Difficulty in expressing scale (and in setting
baselines or zeroing out data)
 Difficulty in expressing context
 Difficulty in comparing network graphs
(without defining data extraction parameters
and contexts and other factors)
 Highly variant graph visualizations from same
data but different clustering algorithms and
graph layout algorithms
 Limits of structural-relational
network analysis assumptions
 Theoretically
 Practically
197

 How should a researcherchoose
which social media platform(s)to
tap for particular research
questions?
 How important is the cultures of sharing
onthe site? The practices?
 What is the role of the data types shared?
 How important are the demographics of
the site users?
 How important is the end user license
agreement (EULA)of the social media
platformservice provider?
 What are the various choices that a
researcher may make in capturing
data from social media platforms?
 What do the various data extraction
parameters mean?
 What data variations may be seenbased
onthe differingparameters?
 How would“optimal” parameters be set
for a particular researchuse case?
198

 What do the various graph metrics
related to the social network mean?
 What does it mean if a network has a lot or a
few vertices? Edges?
 What does it mean if there are some nodes
with high degree? Closeness centrality?
Betweenness centrality? Eigenvector
centrality? How are self-loops to be
understood (individually and globally within
the graph)?
 What does it mean if there is “high” or “low”
geodesic distance between nodes? What
does it mean if the maximum geodesic
distance (graph diameter) is “high” or “low”?
What does it mean that there is the
assumption of Euclidean space in a relational
social graph?
 What does it mean if a network has a lot of
clusters or a few clusters? What does it
mean if a network has certain types of
clusters?
 What does it mean if there are relatively
large connected components? Or none at
all? Or a lot of small connected components?
 What does it mean if a network has various
motif patterning? Certain numbers in its
motif census?
 What does it mean if a network has a lot of
“isolates” and “pendants” and “whiskers”?
 What does it mean if a network has dense
ties? Sparse ties? When might each network
condition (or “state”) be desirable?
199

 What (otherwiselatent/hidden) insights
may be extracted fromindividual nodes?
The motifs? The clusters? The overall
social graph? Outsideconnected graphs?
 What are someways of profiling an
individual or entity across socialmedia
platforms (and how that individual or
group instantiates across different
platforms)?
 What are waysto capture “ego” biasesor
preferences(assumedtoexistinall egos)?
 How may individualsorentitiesbe trackedto
personallyidentifiable information(PII) from
theirlong-termpseudonymousor temporally
masked“throwaway” identities?
 What are somecreative ways to use
dynamic filtering to interact with the
collected data in the NodeXL graph pane?
 What about the geolocational information?
 What about the time zone information?
 What about the usesof diurnal rhythms to an
account’s posts to project (and maybe identify)
location?
 What about time-of-originof the user account on
the social mediaplatform?
 What about changes to a social network over
time?
200

 How may real-world events be
tracked or monitored through social
media contents?
 How may dominant members (leaders) of an
ad hoc discussion and unfolding event be
identified?
 How may salient messages be extracted
based on sentiment (positive to negative
continuum; valence), emotion, and intensity?
If information is coded, how may those be
understood (with accuracy and speed)?
 How may public “calls to action” be extracted
(and related to in-world events)? How may
the individuals and groups calling the actions
be identified to personally identifiable
information (PII)—to a real person in the real
world?
 How may events be predicted (and followed
through to atrophication or emergence)?
 How may cooperating groups coordinating
across social media be identified?
 How may gists of communications be
understood (even with images, videos, and
multimedia)?
 …all…in (interactive) real-time?
201

 What are some ways to
characterizegeographically-based
characteristicsof members in
particularregions based on their
social media-based
communications? Cultures?
Subcultures? Languages?
 Are there some unique aspectsto
the (meta-)(trace-)(content-)(user-)
data extractedfromrespective
social media platforms?
 How may multi-medial content analysis
be applied to magnify the affordances of
“structural analysis” (enabledby network
analysis)?
202

 Do the technologicalaffordances
and opt-in populations
participatingon respective social
media platformsresult in certain
types of data,datastructures,and
resulting knowability?
 Are there ways to use the knowledge of
such data tendencies to better
understandthe extracteddata andtheir
limitations?
 Understandingthe social media
platforms,are there other ways to
scrape Web data (Python, R) that
may enhance what is knowable by
NodeXL alone?
203

 Are the user networks(on social
media platformslike Facebook,
Twitter, YouTube, Flickr and
Wikipedia) based on recent
interchanges/ recent messaging
(#hashtagdiscussions)or
formalized declared relationships
(follower/following)?
 What is a Facebook fan page
network? What is a Facebook
group network? What is a
Facebook personal network? What
is a Facebook timeline network?
 What are related tagsnetworks (on
Flickr)? What are user networks on
Flickr?
204

 What are “video networks” on
YouTube? What are “user
networks”on YouTube?
 What is an “article-articlenetwork”
on Wikipedia? What is a “co-editor
– article” two-mode network on
Wikipedia?
 Also queryable onanything built on
MediaWiki understructure
 What is a blog network, and what
do its elements (users, comments,
edits, messaging, multimedia
contents,etc.)show?
 What is an “httpnetwork,” and
what does this webpage network
show?
 What is anInternet network,and how do
these reveal insights about an ego or
entity? About social influence?
205

 Who are the egos and entities in a
user network on a particular social
media platform?
 What sorts of “agents” are inthe
network? Human,‘bot, cyborg, or other?
 What assertions may be made
about a particularonline social
network? How much confidence
would you have in those
assertions?
 How couldyoucheck your assertions
outside of the data extractions from
NodeXL?What are some ways toverify
data? [Are there other ways to access
relateddata inorder to (dis)confirminitial
findings?]
 What messages (microblogging
messages, imagery, video links, and
URLs) may be capturedfrom a
social media account,and what
may be understoodfrom those
messages?
206

 What information may be captured
from metadata [tags/label data, trace
data, profile information,
exchangeable image file format
(EXIF) data, locational data, and
others] in a social media platform,
and what may be understood from
that metadata?
 What other types of information may
be extrapolated from publicly-facing
social media? Also, what may be
seen from private channels (for
researchers with the credentials
enabling insider access to respective
accounts)?
 What may be learned by conducting
cross-sectional data extractions
across time? What may be learned
by conducting continuous data
extractions of a social network across
a period?
 When do NodeXL macros (sequences of pre-
set directions to run and re-run on a
computer) come in handy?
207

 Has a full datasetbeen obtained?
Why so (in some rare cases)? Why
not (in more common cases)?
 What are some of the
technologicaldependencies in the
data extractionand graph
visualizing? How may the
limitations be mitigated?
 How much statisticalexplanationis
needed for graphclarity?
 Are there ways to headoff negative
learning and misconceptions basedon
viewing social maps / graphs alone
(without access to the underlying
dataset)?
 How may NodeXL be used in a
complementary way with other
research tools,methods, and ways
of knowing?
208

 Identifying influential nodes in an
ad hoc #hashtagnetwork or
keyword network (aka “mayorsof
hashtags”)and profiling their
accountsand Tweetstreams,
image streams,post histories,and
wall postings(across social media
platforms)
 Identifying collaboratinggroupsof
agents (human-,bot-, and cyborg-)
and their in-world counterparts
(throughpersonally identifiable
information/ PII) and analyzing
strategiesand tactics
209

 Extractingcommunications
streams(in multimodal media) and
conductingtheme extractionsand
topic modeling; network text
analysis; text / contentanalyses;
sentiment and emotion analyses,
and others
 Studying extractedindividual,
group,domain, and language
lexicons
210

 Ideas?
 Requests for demos? Graph
visualization walk-throughs? Light
data analysis?
211

Informationis beautiful, too...
212
For informationalnetwork graphs,not either “beauty”or “data”
but “both and” … but how to integrate?

 About the network graphs: All
depicted graphs were created by the
poster presenter using NodeXL Basic
(with no or very limited “artifice”
effects). On Slide 172, the graph was
created using a beta version of
NodeXL Pro, with its sentiment
analysis feature. These graph
visualizations and their underlying
datasets are available on the NodeXL
Graph Gallery. Interactive graphs
(zoomable, pannable) may be seen in
the experimental version of uploaded
graphs (in GraphML).
 Disclaimers: The presenter has no
formal tie to NodeXL or the Social
Media Research Foundation. The
NodeXL tool functionalities and the
enablements of the social media
platform APIs are constantly
changing, and what is depicted here
only represents some current
capabilities.
213

 Dr. Shalin Hai-Jew
 Instructional Designer
 iTAC, Kansas State University
 212 Hale / Farrell Library
 1117 Mid-Campus Drive North
 785-532-5262
 shalin@k-state.edu
 Thanks to the NodeXL team for
creating and evolving a free, open-
source, and multi-faceted Excel add-
on! Also, I am grateful to the IDRH
and its conference organizing
committee for accepting this digital
poster session. Note: This is
designed as a “digital leave behind”
for use after the event; during the
event, the graph images were used
as conversation starters.
 A related chapter in a forthcoming
book: This digital poster / slideshow
is linked to a forthcoming chapter in
a book slated for release in late 2016.
214

Beauty as a Bridge to NodeXL

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (20)

Semelhante a Beauty as a Bridge to NodeXL

Semelhante a Beauty as a Bridge to NodeXL (20)

Mais de Shalin Hai-Jew

Mais de Shalin Hai-Jew (20)

Último

Último (20)

Beauty as a Bridge to NodeXL