Shalin Hai-Jew Kansas State University 2014 National Extension Technology Conference May 2014 #NETC2014

1. Understanding
Public Sentiment:
Conducting a Related-
Tags Content Network
Extraction and Analysis on
Flickr
Shalin Hai-Jew
Kansas State University
2014 National ExtensionTechnology Conference
May 2014

2. Presentation Overview
• This presentation focuses on how to understand public
sentiment through a related-tags content network
analysis of public Flickr photos and videos. NodeXL is
used to conduct data extractions and visualizations of
user-tagged Flickr contents and the resulting “noisy”
folksonomies.What mental connections may be made
about particular issues based on analysis of text-
annotated graphs?
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
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3. Audience Self-Intros
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4. DefiningTerms
• Public sentiment: community attitude (and understanding)
• Tag: electronic label (a form of metadata)
• Related tags: label which co-occurs with some frequency with
another tag (co-occurrence, association)
• Folksonomy: informal and inexpert classification system from
electronic tags and keywords
• Word sense: the gist of a term based on its usage and
nuanced understandings (and definitional evocations)
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5. DefiningTerms(cont.)
• Flickr: a digital content-sharing (photos and videos) social
media platform
• NodeXL: Network Overview, Discovery and Exploration for
Excel, an open-source (Ms-PL) and free add-on to Excel
(available on Microsoft’s CodePlex)
• Data extraction: the drawing out of raw data from a
database; a data crawl
• Graph: a two-dimensional diagram depicting data
• API: application programming interface
• Flickr API key and secret: a unique access code for the data
extraction through NodeXL (email verified)
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Analysis on Flickr
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6. DefiningTerms(cont.)
• Social network graph: a 2D or 3D diagram showing social
entities and relationships (nodes-links, vertices-edges)
• Related tags network graph: the egocentric network of a
specified tag (as vertex); a text-based visualization showing
entities and inter-relationships between tags (metadata labels
/ terms)
• (Social, content, other) network analysis: study of relations
between entities (often expressed as a node-link diagram)
• Content network: the representation of relations between
content-based entities in a graph
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Analysis on Flickr
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7. DefiningTerms(cont.)
• Metadata: information about data often used to enhance
archival of that data: understanding of and access to those
resources
• Data leakage: information released in an unintended or
indirect way
• Word sense: the gist of a term based on its usage and
nuanced understandings (and definitional evocations)
• Partition: the segmentation of a graph into separate parts
based on similarity clustering (grouping)
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Analysis on Flickr
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8. The Process
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Analysis on Flickr
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9. Text-BasedTags at theTag Link
on Flickr
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
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10. Sample
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
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11. A Quick “How-to” on Interpreting
RelatedTags Graphs
• Center-periphery dynamic (and influence)
• Large vs. small clusters (and tag frequency)
• Clustering around frequency of association and co-
occurrence and represented in spatial proximity and color
• Social effects of tagging
• Structure (relational) and semantic (meaning,
definitional) and syntactic (language mechanics) mining
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
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12. Flickr
• 10 years old as of Feb. 10, 2014
• 92 million users across 63 countries
• 2 million groups
• 1 million photos shared a day
• Available in 10 languages
• Created by Ludicorp and owned now byYahoo, Inc.
• Offers a terabyte per user
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13. Early Observations? Questions?
Affordances
• What sorts of information can
you know from such related tags
networks?
• How direct or indirect is this
information?
• How confident would you be of
the results, and why?
Constraints
• Any early ideas on limits to
related tags network analysis?
• How accurately may inferences
be made about public sentiments
and understandings by such
related tags word associations?
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Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr

14. Sample RelatedTags Networks
(hopefully somewhat related to National Extension interests
and within the limits of available Flickr tags)
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Analysis on Flickr
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15. YourTurn!
• Your table will be assigned several of the following graphs
• Find the core related tags search term (sometimes at the
center of the graph unless partitions are used)
• Identify the main groups and label them in your own words to
the best of your ability
• Any sense of the public sentiment? Public understandings of
the topic?
• See any patterns? Anomalies? Anything worth further
investigation?
• Be ready to share your findings with the group
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16. aquaculture
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Analysis on Flickr
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1

17. personal finance
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Analysis on Flickr
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18. PTSD
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Analysis on Flickr
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19. health
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Analysis on Flickr
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20. mortgage
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Analysis on Flickr
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21. animal control
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22. safety
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Analysis on Flickr
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23. lawn
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Analysis on Flickr
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24. forest
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Analysis on Flickr
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25. food
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Analysis on Flickr
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26. county fair
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11a

27. County fair
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Analysis on Flickr
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11b

28. family
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Analysis on Flickr
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29. garden
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Analysis on Flickr
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30. agriculture
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Analysis on Flickr
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31. entomology
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Analysis on Flickr
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32. home
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33. exercise
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34. community
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35. horticulture
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Analysis on Flickr
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36. farming
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20a

37. farming
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20b

38. parenting
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Analysis on Flickr
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39. pest
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Analysis on Flickr
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40. livestock
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Analysis on Flickr
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41. craft
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Analysis on Flickr
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42. disability
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25a

43. disability
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25b

44. Think of the Possibilities with…
• Generic terms
• Controversial terms
• Competing terms
• Multiple languages
• Public individuals
• Project names
• New scientific terms
• Cultural memes
• Photo or video contests
(elicitations for certain
multimedia contents)
• Content-based video
conversations and video
replies
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45. A Research Angle
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46. GeneralWorkflow
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47. What May be Asserted?
• Apparent patterns
• Clusters or groups (textual and visual)
• Anomalous connections
• “Missing” information (what is not showing up)
• Apparent sentiments and attitudes (emotion- and value-laden
words)
• Apparent implied cultures
• Any ideas on how to confirm or disconfirm findings from
related tags network analysis?
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48. Types of Applied Analyses
• Inferences based on evidence and reasoning (induction,
deduction)
• Emergent pattern analysis
• A priori pattern analysis
• Term and phrase disambiguation (of unstructured text)
• Comparisons and contrasts
• Text analyses (frequency counts, word trees, sentiment,
others)
• Image analyses
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49. Text and Image-BasedVersions
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50. Re-Visualization in NodeXL
• Multi-graph visualizations
• Text-based vertices (nodes)
• Image-based vertices (nodes)
• Labeled links (edges)
• Differing layout algorithms (usually Fruchterman-Reingold or Harel-
Koren Fast Multiscale)
• Dynamic filtering (to control variable range)
• Analysis of particular “stand-alone” clusters
• Analysis of peripheral nodes / vertices
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51. Event-Based RelatedTags
Networks
• Images related to an event
• Video related to an event
• The tags related to the event
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52. TagText Analysis
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Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
related
terms

53. Analysis overTime
• Changing related tags networks over time
• Changing terminology in the tags
• Trends and patterns
• Term manifestations on different content-sharing
platforms (research method transferability)
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54. Other PotentialVisualizations
Outside NodeXL
• Tag clouds (word frequency count)
• Tag word tree (close related word constructs)
• Tag geography (maps of where tags come from)
• (These additional visualizations are possible depending on the
nature of the dataset and access to text analysis and
visualization tools.)
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Analysis on Flickr
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55. Using NodeXL for the Related
Tags Data Extraction on Flickr
A Step-by-StepWalkthrough
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Analysis on Flickr
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56. Starting the Data Crawl
• Download and install NodeXL (have a recent version of
Excel)
• Open NodeXL
• Go to NodeXL ribbon
• File > Import > From Flickr RelatedTags Network …
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57. Defining Parameters of the
(RelatedTags) Data Extraction
• Fill in the search term (vertex tag)
• Define parameters
• Select degrees (1 degree = egocentric network / ego neighborhood;
1.5 degrees = transitivity among alters of the ego neighborhood; 2.0
degrees = the ego neighborhoods of the alters)
• Adding a sample image from each tag in the network
• Fill in the Flickr API key (from Flickr’s The App Garden)
• Click “Okay”
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58. Image: Starting the Crawl
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Network Degree

59. Image: Saving the Data
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Results of the Data Extraction

60. Data Processing
• Go to the Analysis section in the ribbon
• Select Graph Metrics
• Check the boxes next to the graph metrics that you want
to extract
• Click “Calculate Metrics”
• Save
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61. Image: Processing the Data
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Graph Metrics
(post-processing)

62. Image:The Graph MetricsTable
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63. Data Processing (cont.)
• Identify clusters (groups) by…
• InAnalysis (in the NodeXL ribbon), under Groups, select the
parameters for the grouping
• ByVertex Attribute
• By Connected Component
• By Cluster (select clustering algorithm)
• By Motif
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64. Image: Identifying Clusters
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65. OutputtingVisualizations
• Create visualization(s)
• In graph pane (at the right), click “ShowGraph”
• Experiment with graph types
• Add imagery to vertices (nodes)
• Add details to edges (links)
• Change labels in Autofill Columns (underVisual
Properties)
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66. Graph Pane
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67. Image: Graph Sampler
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68. Image: Graph Sampler
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69. Image: Graph Sampler
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70. Image: Graph Sampler
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71. Exporting Imagery
• Right click in the image pane to
• Copy image to clipboard
• Save image to file
• Capture screenshot
• Save Excel file
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72. Time for aWalk-through?
• Any terms for our related tags network on Flickr?
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73. Caveats to the Uses of Related
Tags Network Analysis for
Research
social computing marketing public relations
academic research data journalism
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74. Potential Structural Sources of
Noise and Error
• Limited dataset to certain types of multimedia (created by certain
subset of the main population)
• Researcher conceptualization and analysis error
• Inexpert tagging and noisy data (not fully disambiguated, not
mutually exclusive terms, not aligned word forms)
• Multilingual data
• Incomplete extraction (not false positives, but false negatives)
• Ambiguity
• Dynamism (changes over time)
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75. Some Resources
• NodeXL on CodePlex
• NodeXL Graph Gallery
• Social Media Research Foundation (SMRF)
• Flickr
• Rodrigues, E.M. & Milic-Frayling, N. (2011). Flickr: Linking
people, photos, and tags. Ch. 13. In D.L. Hansen, B.
Schneiderman, & M.A. Smith’s Analyzing Social Media
Networks with NodeXL: Insights from a ConnectedWorld. 201 –
223.
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76. Conclusion and Contact
• Dr. Shalin Hai-Jew
• Instructional Designer
• InformationTechnologyAssistance
Center
• 212 Hale Library
• Kansas State University
• 785-532-5262
• shalin@k-state.edu
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