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Beauty as a Bridge to NodeXL

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What is NodeXL (Network Overview, Discovery and Exploration for Excel)?
Graph aesthetics in NodeXL
Visual pleasure
Cognitive pleasure
Bridging to NodeXL for research and analysis

Publicada em: Tecnologia

Beauty as a Bridge to NodeXL

  1. 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. 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. 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. 4. (Part 1)
  5. 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. 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. 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. 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. 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. 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. 11. Visual pleasure Cognitive pleasure (Part 2) 11
  12. 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? 12
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  140. 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
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  142. 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. 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. 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. 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. 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. 147 (Part 3)
  148. 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. 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. 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. 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. 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 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! ! 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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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/well point career Yes t eamworkonli ne ht t p: //pbs.t wimg. com/prof ile_image 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/alex andraheber Yes abcnews24 ht t p: //pbs.t wimg. com/prof ile_image 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/jobs _secret ary Yes mnjobconnect er ht t p: //pbs.t wimg. com/prof ile_image 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/jobs 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/t hwj obs Yes railsjobscom ht t p: //pbs.t wimg. com/prof ile_image 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor ThisPer son ht t ps: //twit t er.com/rorjo bs Yes velvet jobs ht t p: //pbs.t wimg. com/prof ile_image 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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 OpenTwit t er Pagefor 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
  153. 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. 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. 155. datasheets<-> social network graph in graph pane (interactivityby highlighting for cross-referencing) 155
  156. 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. 157. “threshold”related tagsnetwork on Flickr (1.5deg.) 157
  158. 158. 158 “Internet”relatedtagsnetwork on Flickr (1.5deg.)
  159. 159. “network”related tags network on Flickr (2 deg.) / with group or clusterpartitioning 159
  160. 160. “Yangtze”related tags network on Flickr (1 deg.) 160
  161. 161. 161 “night” related tagsnetwork on Flickr (1.5deg.)
  162. 162. 162 “flicker” related tags network on Flickr (1.5 deg.) / group partitioning
  163. 163. 163 “tomato” relatedtagsnetwork on Flickr (1.5deg.) / manual group labeling
  164. 164. “Greece”related tagsnetworkon Flickr (1.5deg.) 164
  165. 165. 165 “weekend” video network on YouTube (unlimited)
  166. 166. 166 “air quality”video network on YouTube (unlimited)
  167. 167. “quantumcomputing”video network on YouTube (unlimited) 167
  168. 168. “bridge”video network on YouTube (unlimited) 168
  169. 169. @johnkerry ad hoc user network on Twitter based on recent messaging 169
  170. 170. 170 @AlexSalmonduser ego neighborhood network on Twitter (1 deg.)
  171. 171. 171 #elicitation hashtagsearch on Twitter (basic network) / one conversation
  172. 172. 172 #sentiment hashtagsearch on Twitter (based network) / built-in sentiment analysis based on a lexicon dictionary in NodeXL Pro
  173. 173. 173 #ebola hashtagsearch on Twitter (basic network) / top 10 group keywords
  174. 174. #rivalry hashtagsearch on Twitter (basic ad hoc network) / who’s talking 174
  175. 175. 175 #Sweden hashtagsearchon Twitter (basic network) / 4,854vertices 0 edges / people talking to themselves in online public spaces?
  176. 176. 176 @wacomuser network on Twitter (2 deg., 10-personslimit)
  177. 177. “Hersheys”fan page on Facebook (dataeven without node-level labeling) 177
  178. 178. #virscan hashtagsearch on Twitter (basic network) 178
  179. 179. 179 #alert hashtagsearch on Twitter (basic network) / collapsed groups
  180. 180. 180 #NSA hashtagsearch on Twitter (basic network) / keywords in top 10 groups
  181. 181. 181 #draft hashtagsearchon Twitter (basic network) / multidimensional data
  182. 182. 182 #avian #flu hashtagsearch on Twitter (basic network) / Boolean search data
  183. 183. “ambchrishill” ad hoc user network on Twitter including friends (formal declared / verified network) 183
  184. 184. 184 #share hashtagsearchnetwork on Twitter(1 deg.)
  185. 185. #Everest hashtagsearchon Twitter (basic network), with key terms in top 10 groupshighlighted 185
  186. 186. 186 #14dayshashtagsearch on Twitter (basic network)
  187. 187. “Alexis_Tsipras”article network on Wikipedia (1 deg.) / about the individual 187
  188. 188. 188“Chinese_yuan” article network on Wikipedia (1 deg.) / topic-based
  189. 189. 189“Malaysia_Airlines_Flight_370” articlenetwork on Wikipedia (1 deg.) / event-based
  190. 190. 190 “2015_Nepal_earthquake”articlenetwork on Wikipedia (1 deg.) / event-based
  191. 191. 191 “User:Ahunt”articlenetwork on Wikipedia (1 deg.) / human editor network
  192. 192. 192“Wikipedia” article network on Wikipedia (1 deg.) / about Wikipedia
  193. 193. 193“User:BG19bot”article network on Wikipedia (1 deg.) / flagged bot ego neighborhood
  194. 194. 194 syntheticdata for a “privilege walk” / inherited lifestyle features and proximity to power at core
  195. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 211.  Ideas?  Requests for demos? Graph visualization walk-throughs? Light data analysis? 211
  212. 212. Informationis beautiful, too... 212 For informationalnetwork graphs,not either “beauty”or “data” but “both and” … but how to integrate?
  213. 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. 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
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