understanding public sentiment: conducting a related-tags content network extraction and analysis on...

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

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

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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

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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

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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.)

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Using NodeXL for the Related Tags Data

Extraction on FlickrA Step-by-Step Walkthrough

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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

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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)

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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

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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

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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

• shalin@k-state.edu