automated discovery and visualization of communication networks from social media
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Automated Discovery and Visualization of Communication Networks from Social Media
Anatoliy Gruzd @[email protected]
Associate Professor, School of Information ManagementDirector, Social Media LabFaculty of Management / Faculty of Computer Science Dalhousie University
Colloquium on Compromised Data? New paradigms in social media theory and methods. Toronto, October 29, 2013
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Dalhousie University
Faculty of Management
School of Information Management
Social Media Lab
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Social Media Lab
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SocialMediaLab.ca
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New Sage Journal: Big Data & Society
• Open Access & Multidisciplinary
• Editors• Evelyn Ruppert (Sociology, Goldsmiths, UK);
• Paolo Ciuccarelli (Density Design, Milan, IT)
• Anatoliy Gruzd (School of Information Management, Dalhousie
University, CA)
• Adrian Mackenzie (Sociology, Lancaster, UK)
• Richard Rogers (Digital Methods Initiative, Amsterdam, NL)
• Irina Shklovski (Digital Media & Communication Research Group, IT
University of Copenhagen, DK)
• Judith Simon (Institute for Technology Assessment and Systems
Analysis, Karlsruhe Institute of Technology, DE)
• Matt Zook (New Mappings Collaboratory, Geography, Kentucky, US).
Anatoliy Gruzd @dalprof
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Agenda
• Automated Discovery of Communication Networks from
Online Data
• Sense of Community in Online Communities
• Netlytic.org
Anatoliy Gruzd @dalprof
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Growth of Social Media and Social Networks Data
1B users
500M users
Social Media have become an integral part of our daily lives!
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How to Make Sense of Social Media Data?
8Anatoliy Gruzd Twitter: @dalprof
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How to Make Sense of Social Media Data?
Social Network Analysis (SNA)
• Nodes = Group Members/People
• Edges /Ties (lines) =
relations / Connections
9Anatoliy Gruzd Twitter: @dalprof
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• Reduce the large quantity of data into
a more concise representation
• Makes it much easier to understand
what is going on in a group
Advantages of Using Social Network Analysis to
Analyze Social Media Data
Anatoliy Gruzd Twitter: @dalprof
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•Researchers
– Ability to ask and answer deeper questions about the nature and operation of online communities
• How and why one online community emerges and another dies?
• How people agree on common practices and rules in an online community?
• How knowledge and information is shared among group members?
Why Do We Want To Discover Online Social Networks?
Anatoliy Gruzd @dalprof
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Anatoliy Gruzd Twitter: @dalprof #1b1t Twitter Book Club
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Anatoliy Gruzd Twitter: @dalprof 2012 Olympics in London
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Anatoliy Gruzd Twitter: @dalprof #tarsand Twitter Community
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•Common approach: surveys or interviews
•A sample question about students’ perceived social structures (based on C. Haythornthwaite’s 1999 LEEP study protocol)
How Do We Collect Information About Social Networks?
Please indicate on a scale from [1] to [5],
YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS
[1] - don’t know this person
[2] - just another member of class
[3] - a slight friendship
[4] - a friend
[5] - a close friend
Alice D. [1] [2] [3] [4] [5]
…
Richard S. [1] [2] [3] [4] [5]
15Anatoliy Gruzd @dalprof
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Problems with surveys or interviews
• Time-consuming
• Questions can be too sensitive
• Answers are subjective or incomplete
• Participant can forget people and interactions
• Different people perceive events and relationships differently
How Do We Collect Information About Online Social Networks?
19Anatoliy Gruzd Twitter: @dalprof
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Different Types of Online Social Networks
http://www.visualcomplexity.com/vc
•Email networks
•Forum networks
•Blog networks
•Friends’ networks on Facebook, Twitter, etc
•Networks of like-minded people on
How Do We Collect Information About Social Networks?
20Anatoliy Gruzd @dalprof
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Automated Discovery of Social Networks
Emails
Nick
Rick
Dick
• Nodes = People
• Ties = “Who talks to whom”
• Tie strength = The number of
messages exchanged between
individuals
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Automated Discovery of Social Networks
“Many to Many” Communication
ChatMailing listservForum Comments
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Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to)
FROM: SamPREVIOUS POSTER: Gabriel
....
....
....
Posting
header
Content
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Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to)
FROM: SamPREVIOUS POSTER: Gabriel
“ Nick, Gina and Gabriel: I apologize for not backing this up
with a good source, but I know from reading about this topic that … ”
Posting
header
Content
Possible Missing Connections:
• Sam -> Nick
• Sam -> Gina
• Nick <-> Gina 24Anatoliy Gruzd
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Research Question
What content-based features of online interactions can help to uncover nodes and ties between group members?
How Do We Collect Information About Social Networks?
26Anatoliy Gruzd @dalprof
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Automated Discovery of Social Networks
Approach 2: Name Network
FROM: Ann
“Steve and Natasha, I couldn't wait to see your site.
I knew it was going to [be] awesome!”
This approach looks for personal names in the content of the messages to identify social connections between group members.
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•Main Communicative Functions of Personal Names (Leech, 1999)
–getting attention and identifying addressee
–maintaining and reinforcing social relationships
•Names are “one of the few textual carriers of identity” in discussions on the web (Doherty, 2004)
•Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005)
Automated Discovery of Social NetworksApproach 2: Name Network
28Anatoliy Gruzd @dalprof
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Automated Discovery of Social Networks
Name Network Method: Challenges
Kurt Cobain, a lead singer for the rock band Nirvana
chris is not a group member
Santa Monica Public Library
John Dewey, philosopher &educator
mark up language
Solution:- Name alias resolution
29Anatoliy Gruzd @dalprof
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Chain Network
(less connections)
Name Network
(more connections)
Evaluating Name Networks
Example: Youtube comments
Chain Network Name Network
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Evaluating Name NetworksResults from Online Learners Dataset
Dataset
Classes 6
School year Spring 2008
Duration of each
class15 weeks
No. of students
per class17 – 29
Data source
• Bulletin board
messages
• Online
questionnaire
31
No. of all postings
0
500
1000
1500
2000
Class
#1
Class
#2
Class
#3
Class
#4
Class
#5
Class
#6
No. of students
0
10
20
30
Class #1 Class #2 Class #3 Class #4 Class #5 Class #6
Gruzd, A. (2009). Studying Collaborative Learning Using Name Networks.Journal of Education for Library and Information Science 50(4): 243-253.
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Results
• Name networks provide on average 40% more information about social ties in a group as compared to Chain networks
33
“New” Info
82%An addressee has not
posted to the thread
18%An addressee is not the most
recent poster
70%Thread-starting posting
30%A subsequent posting
in the thread
Name Network Chain NetworkQAP correlation ~ 0.5
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Results
• Self-reported networks are almost twice as likely to share the same ties
with Name networks than with Chain networks.
34
Chain NetworkName Network
Self-Reported Network
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Results
• The following social relations were found by the “name network” method
35
• These social relations are considered by many researchers to be crucial in shared knowledge construction and community building thus, name networks can be useful in the assessment of collaborative learning
Learn ● Collaborative Work ● Help
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Agenda
• Automated Discovery of Communication Networks from
Online Data
• Sense of Community in Online Communities
• Netlytic.org
Anatoliy Gruzd @dalprof
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Case Study:
Online Communities Among Blog Readers
•Can a blog support the development of an online community?
•How do we know if a community has emerged among blog readers?
Gruzd, A. (2009). Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog. Proceedings of the Internet Research 10.0 Conference, October 7-11, 2009, Milwaukee, WI, USA.
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Characteristics of Online Community
Virtual Settlement (Jones, 1997)
–virtual common-public-place
–interactivity
–sustained membership
Sense of Community (McMillan & Chavis, 1986)
–feelings of membership & influence
–reinforcement of needs
–shared emotional connection
Anatoliy Gruzd @dalprof
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Comments Posted by Blog Readers
Anatoliy Gruzd @dalprof
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Changes in Social Networks over Time
Anatoliy Gruzd @dalprof
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SNA Statistics
–the posters became more connected and more of them took a stand in a group
Anatoliy Gruzd @dalprof
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Agenda
• Automated Discovery of Communication Networks from
Online Data
• Sense of Community in Online Communities
• Netlytic.org
Anatoliy Gruzd @dalprof
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Netlytica cloud-based analytic tool for automated text analysis &
discovery of social networks from online communication
Ne
two
rk
s
Sta
ts
Co
nte
nt
80Anatoliy Gruzd Twitter: @dalprof
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http://netlytic.org
1) Capture public, online, conversational-type data such as tweets, blog
comments, forum postings, and text messages, etc.
2) Find and explore emerging themes of discussions among individuals
within your data set,
3) Build and visualize communication networks to discover and explore
emerging social connections between individuals.
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Twitter search keywords:
Compromised Data
Anatoliy Gruzd @dalprof
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Twitter search keywords:
#compdata13
Anatoliy Gruzd @dalprof
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Automated Discovery and Visualization of Communication Networks from Social Media
Anatoliy Gruzd @[email protected]
Associate Professor, School of Information ManagementDirector, Social Media LabFaculty of Management / Faculty of Computer Science Dalhousie University
Colloquium on Compromised Data? New paradigms in social media theory and methods. Toronto, October 29, 2013