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

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?

3

Audience Self-Intros


4

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)


5

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)

http://nodexl.codeplex.com/license




6


• 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


7


• 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)


8

The Process


9

Text-Based Tags at the Tag Link

on Flickr


10

Sample


11

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


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 by Yahoo, Inc.

• Offers a terabyte per user

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?



14

Sample Related Tags Networks

(hopefully somewhat related to National Extension interests and within the limits of available Flickr

tags)


15

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


16

aquaculture

1


17

personal finance

2


18

PTSD

3


19

health

4


20

mortgage

5

21

animal control

6


22

safety

7


23

lawn

8


24

forest

9


25

food

10


26

county fair

11a


27

County fair

11b


28

family

12


29

garden

13


30

agriculture

14


31

entomology

15


32

home

16


33

exercise

17


34

community

18


35

horticulture

19


36

farming

20a


37

farming

20b


38

parenting

21


39

pest

22


40

livestock

23


41

craft

24


42

disability

25a


43

disability

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


45

A Research Angle


46

General Workflow


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?


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


49

Text and Image-Based Versions


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


51

Event-Based Related Tags Networks

• Images related to an event

• Video related to an event

• The tags related to the event


52

Tag Text Analysis

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

related terms


53

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)


54

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.)


55

Using NodeXL for the Related Tags Data

Extraction on FlickrA Step-by-Step Walkthrough


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 Related Tags Network …


57

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”

http://www.flickr.com/services/api/misc.api_keys.html

http://www.flickr.com/services/api/misc.api_keys.html


58

Image: Starting the CrawlNetwork Degree


59

Image: Saving the DataResults 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


61

Image: Processing the DataGraph Metrics (post-processing)


62

Image: The Graph Metrics Table


63

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


64

Image: Identifying Clusters


65

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)


66

Graph Pane


67

Image: Graph Sampler


68



69


70



71

Exporting Imagery

• Right click in the image pane to

• Copy image to clipboard

• Save image to file

• Capture screenshot

• Save Excel file


72

Time for a Walk-through?

• Any terms for our related tags network on Flickr?


73

Caveats to the Uses of Related Tags Network Analysis for Research

social computing marketing public relations

academic research data journalism


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)


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 Connected World. 201 – 223.

http://nodexl.codeplex.com/

http://nodexl.codeplex.com/

https://nodexlgraphgallery.org/Pages/Default.aspx

http://www.smrfoundation.org/

https://www.flickr.com/


76

Conclusion and Contact

• Dr. Shalin Hai-Jew

• Instructional Designer

• Information Technology Assistance Center

• 212 Hale Library

• Kansas State University

• 785-532-5262

• [email protected]

mailto:[email protected]

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

Education