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Understanding Public Sentiment:
Conducting a Related-Tags Content Network
Extraction and Analysis on Flickr
Shalin Hai-Jew
Kansas State University
2014 National Extension Technology Conference
May 2014
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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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?
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Audience Self-Intros
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Defining Terms
• 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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Defining Terms (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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Defining Terms (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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Defining Terms (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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The Process
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Text-Based Tags at the Tag Link
on Flickr
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Sample
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A Quick “How-to” on Interpreting Related Tags
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
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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 by Yahoo, Inc.
• Offers a terabyte per user
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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?
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Sample Related Tags Networks
(hopefully somewhat related to National Extension interests and within the limits of available Flickr
tags)
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Your Turn!
• 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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aquaculture
1
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personal finance
2
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PTSD
3
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health
4
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mortgage
5
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animal control
6
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safety
7
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lawn
8
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forest
9
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food
10
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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county fair
11a
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County fair
11b
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family
12
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garden
13
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agriculture
14
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entomology
15
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home
16
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exercise
17
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community
18
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horticulture
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farming
20a
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farming
20b
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parenting
21
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pest
22
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livestock
23
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craft
24
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disability
25a
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disability
25b
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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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A Research Angle
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General Workflow
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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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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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Text and Image-Based Versions
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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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Event-Based Related Tags 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
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Tag Text Analysis
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
related terms
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Analysis over Time
• 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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Other Potential Visualizations 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
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Using NodeXL for the Related Tags Data
Extraction on FlickrA Step-by-Step Walkthrough
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Starting the Data Crawl
• Download and install NodeXL (have a recent version of Excel)
• Open NodeXL
• Go to NodeXL ribbon
• File > Import > From Flickr Related Tags Network …
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Defining Parameters of the (Related Tags) 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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Image: Starting the CrawlNetwork Degree
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Image: Saving the DataResults of the Data Extraction
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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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Image: Processing the DataGraph Metrics (post-processing)
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Image: The Graph Metrics Table
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Data Processing (cont.)
• Identify clusters (groups) by…
• In Analysis (in the NodeXL ribbon), under Groups, select the parameters for the grouping
• By Vertex Attribute
• By Connected Component
• By Cluster (select clustering algorithm)
• By Motif
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Image: Identifying Clusters
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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Outputting Visualizations
• Create visualization(s)
• In graph pane (at the right), click “Show Graph”
• Experiment with graph types
• Add imagery to vertices (nodes)
• Add details to edges (links)
• Change labels in Autofill Columns (under Visual Properties)
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Graph Pane
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Image: Graph Sampler
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Image: Graph Sampler
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Image: Graph Sampler
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Image: Graph Sampler
Understanding Public Sentiment: Conducting a Related-Tags Content Network Extraction and Analysis on Flickr
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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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Time for a Walk-through?
• Any terms for our related tags network on Flickr?
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Caveats to the Uses of Related Tags Network Analysis for Research
social computing marketing public relations
academic research data journalism
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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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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 Connected World. 201 – 223.
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Conclusion and Contact
• Dr. Shalin Hai-Jew
• Instructional Designer
• Information Technology Assistance Center
• 212 Hale Library
• Kansas State University
• 785-532-5262