1.
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

2.
 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

3.
 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

4.
(Part 1)

5.
 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

6.
 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

7.
 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

8.
 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

9.
 “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

10.
 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

11.
Visual pleasure
Cognitive pleasure
(Part 2)
11

12.
 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?
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140.
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

141.
141

142.
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

143.
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

144.
 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

145.
 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

146.
 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

147.
147
(Part 3)

148.
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

149.
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

150.
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

151.
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
G14 0, 136, 227 Solid Square
G15 0, 100, 50 Solid Square
G16 0, 176, 22 Solid Square
G17 191, 0, 0 Solid Square
G18 230, 120, 0 Solid Square
G19 255, 191, 0 Solid Square
G20 150, 200, 0 Solid Square
151

152.
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
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152

153.
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

154.
 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

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

156.
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

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

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

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

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

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

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

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

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

165.
165
“weekend” video network on YouTube (unlimited)

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

167.
“quantumcomputing”video network on YouTube (unlimited)
167

168.
“bridge”video network on YouTube (unlimited)
168

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

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

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

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

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

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

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

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

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

178.
#virscan hashtagsearch on Twitter (basic network)
178

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

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

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

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

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

184.
184
#share hashtagsearchnetwork on Twitter(1 deg.)

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

186.
186
#14dayshashtagsearch on Twitter (basic network)

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

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

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

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

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

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

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

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

195.
 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

196.
 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

197.
 “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

198.
 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

199.
 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

200.
 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

201.
 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

202.
 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

203.
 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

204.
 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

205.
 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

206.
 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

207.
 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

208.
 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

209.
 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

210.
 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

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

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

213.
 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

214.
 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

215.
215