Understanding
Public Sentiment:
Conducting a Related-
Tags Content Network
Extraction and Analysis on
Flickr
Shalin Hai-Je...
Presentation Overview
• This presentation focuses on how to understand public
sentiment through a related-tags content net...
Audience Self-Intros
3
DefiningTerms
• Public sentiment: community attitude (and understanding)
• Tag: electronic label (a form of metadata)
• Re...
DefiningTerms(cont.)
• Flickr: a digital content-sharing (photos and videos) social
media platform
• NodeXL: Network Overv...
DefiningTerms(cont.)
• Social network graph: a 2D or 3D diagram showing social
entities and relationships (nodes-links, ve...
DefiningTerms(cont.)
• Metadata: information about data often used to enhance
archival of that data: understanding of and ...
The Process
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
8
Text-BasedTags at theTag Link
on Flickr
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extracti...
Sample
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
10
A Quick “How-to” on Interpreting
RelatedTags Graphs
• Center-periphery dynamic (and influence)
• Large vs. small clusters ...
Flickr
• 10 years old as of Feb. 10, 2014
• 92 million users across 63 countries
• 2 million groups
• 1 million photos sha...
Early Observations? Questions?
Affordances
• What sorts of information can
you know from such related tags
networks?
• How...
Sample RelatedTags Networks
(hopefully somewhat related to National Extension interests
and within the limits of available...
YourTurn!
• Your table will be assigned several of the following graphs
• Find the core related tags search term (sometime...
aquaculture
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
16...
personal finance
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flic...
PTSD
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
18
3
health
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
19
4
mortgage
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
20
5
animal control
21
6
safety
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
22
7
lawn
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
23
8
forest
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
24
9
food
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
25
10
county fair
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
26...
County fair
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
27...
family
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
28
12
garden
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
29
13
agriculture
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
30...
entomology
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
31
...
home
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
32
16
exercise
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
33
17
community
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
34
18
horticulture
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
3...
farming
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
36
20a
farming
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
37
20b
parenting
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
38
21
pest
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
39
22
livestock
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
40
23
craft
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
41
24
disability
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
42
...
disability
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
43
...
Think of the Possibilities with…
• Generic terms
• Controversial terms
• Competing terms
• Multiple languages
• Public ind...
A Research Angle
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flic...
GeneralWorkflow
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flick...
What May be Asserted?
• Apparent patterns
• Clusters or groups (textual and visual)
• Anomalous connections
• “Missing” in...
Types of Applied Analyses
• Inferences based on evidence and reasoning (induction,
deduction)
• Emergent pattern analysis
...
Text and Image-BasedVersions
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Anal...
Re-Visualization in NodeXL
• Multi-graph visualizations
• Text-based vertices (nodes)
• Image-based vertices (nodes)
• Lab...
Event-Based RelatedTags
Networks
• Images related to an event
• Video related to an event
• The tags related to the event
...
TagText Analysis
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flic...
Analysis overTime
• Changing related tags networks over time
• Changing terminology in the tags
• Trends and patterns
• Te...
Other PotentialVisualizations
Outside NodeXL
• Tag clouds (word frequency count)
• Tag word tree (close related word const...
Using NodeXL for the Related
Tags Data Extraction on Flickr
A Step-by-StepWalkthrough
Understanding Public Sentiment: Cond...
Starting the Data Crawl
• Download and install NodeXL (have a recent version of
Excel)
• Open NodeXL
• Go to NodeXL ribbon...
Defining Parameters of the
(RelatedTags) Data Extraction
• Fill in the search term (vertex tag)
• Define parameters
• Sele...
Image: Starting the Crawl
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysi...
Image: Saving the Data
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis o...
Data Processing
• Go to the Analysis section in the ribbon
• Select Graph Metrics
• Check the boxes next to the graph metr...
Image: Processing the Data
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analys...
Image:The Graph MetricsTable
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Anal...
Data Processing (cont.)
• Identify clusters (groups) by…
• InAnalysis (in the NodeXL ribbon), under Groups, select the
par...
Image: Identifying Clusters
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analy...
OutputtingVisualizations
• Create visualization(s)
• In graph pane (at the right), click “ShowGraph”
• Experiment with gra...
Graph Pane
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on Flickr
66
Image: Graph Sampler
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on ...
Image: Graph Sampler
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on ...
Image: Graph Sampler
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and
Analysis on ...
Image: Graph Sampler
70
Exporting Imagery
• Right click in the image pane to
• Copy image to clipboard
• Save image to file
• Capture screenshot
•...
Time for aWalk-through?
• Any terms for our related tags network on Flickr?
Understanding Public Sentiment: Conducting a R...
Caveats to the Uses of Related
Tags Network Analysis for
Research
social computing marketing public relations
academic res...
Potential Structural Sources of
Noise and Error
• Limited dataset to certain types of multimedia (created by certain
subse...
Some Resources
• NodeXL on CodePlex
• NodeXL Graph Gallery
• Social Media Research Foundation (SMRF)
• Flickr
• Rodrigues,...
Conclusion and Contact
• Dr. Shalin Hai-Jew
• Instructional Designer
• InformationTechnologyAssistance
Center
• 212 Hale L...
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Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr

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

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Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr

  1. 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. 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 2
  3. 3. Audience Self-Intros 3
  4. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 4
  5. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 5
  6. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 6
  7. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 7
  8. 8. The Process Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 8
  9. 9. Text-BasedTags at theTag Link on Flickr Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 9
  10. 10. Sample Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 10
  11. 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 11
  12. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 12
  13. 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? 13 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
  14. 14. Sample RelatedTags Networks (hopefully somewhat related to National Extension interests and within the limits of available Flickr tags) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 14
  15. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 15
  16. 16. aquaculture Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 16 1
  17. 17. personal finance Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 17 2
  18. 18. PTSD Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 18 3
  19. 19. health Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 19 4
  20. 20. mortgage Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 20 5
  21. 21. animal control 21 6
  22. 22. safety Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 22 7
  23. 23. lawn Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 23 8
  24. 24. forest Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 24 9
  25. 25. food Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 25 10
  26. 26. county fair Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 26 11a
  27. 27. County fair Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 27 11b
  28. 28. family Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 28 12
  29. 29. garden Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 29 13
  30. 30. agriculture Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 30 14
  31. 31. entomology Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 31 15
  32. 32. home Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 32 16
  33. 33. exercise Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 33 17
  34. 34. community Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 34 18
  35. 35. horticulture Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 35 19
  36. 36. farming Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 36 20a
  37. 37. farming Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 37 20b
  38. 38. parenting Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 38 21
  39. 39. pest Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 39 22
  40. 40. livestock Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 40 23
  41. 41. craft Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 41 24
  42. 42. disability Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 42 25a
  43. 43. disability Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 43 25b
  44. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 44
  45. 45. A Research Angle Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 45
  46. 46. GeneralWorkflow Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 46
  47. 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? Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 47
  48. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 48
  49. 49. Text and Image-BasedVersions Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 49
  50. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 50
  51. 51. Event-Based RelatedTags Networks • Images related to an event • Video related to an event • The tags related to the event Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 51
  52. 52. TagText Analysis Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 52 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 related terms
  53. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 53
  54. 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.) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 54
  55. 55. Using NodeXL for the Related Tags Data Extraction on Flickr A Step-by-StepWalkthrough Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 55
  56. 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 … Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 56
  57. 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” Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 57
  58. 58. Image: Starting the Crawl Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 58 Network Degree
  59. 59. Image: Saving the Data Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 59 Results of the Data Extraction
  60. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 60
  61. 61. Image: Processing the Data Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 61 Graph Metrics (post-processing)
  62. 62. Image:The Graph MetricsTable Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 62
  63. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 63
  64. 64. Image: Identifying Clusters Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 64
  65. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 65
  66. 66. Graph Pane Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 66
  67. 67. Image: Graph Sampler Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 67
  68. 68. Image: Graph Sampler Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 68
  69. 69. Image: Graph Sampler Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 69
  70. 70. Image: Graph Sampler 70
  71. 71. Exporting Imagery • Right click in the image pane to • Copy image to clipboard • Save image to file • Capture screenshot • Save Excel file Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 71
  72. 72. Time for aWalk-through? • Any terms for our related tags network on Flickr? Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 72
  73. 73. Caveats to the Uses of Related Tags Network Analysis for Research social computing marketing public relations academic research data journalism Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 73
  74. 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) Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 74
  75. 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. Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 75
  76. 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 Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr 76

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