Top Banner
Automated Discovery and Visualization of Communication Networks from Social Media Anatoliy Gruzd @dalprof [email protected] Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University Colloquium on Compromised Data? New paradigms in social media theory and methods. Toronto, October 29, 2013
42

Automated Discovery and Visualization of Communication Networks from Social Media

Aug 27, 2014

Download

Social Media

Anatoliy Gruzd

 
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 2: Automated Discovery and Visualization of Communication Networks from Social Media

Dalhousie University

Faculty of Management

School of Information Management

Social Media Lab

Page 3: Automated Discovery and Visualization of Communication Networks from Social Media

Social Media Lab

Page 4: Automated Discovery and Visualization of Communication Networks from Social Media

SocialMediaLab.ca

Page 5: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 6: Automated Discovery and Visualization of Communication Networks from Social Media

Agenda

• Automated Discovery of Communication Networks from

Online Data

• Sense of Community in Online Communities

• Netlytic.org

Anatoliy Gruzd @dalprof

Page 7: Automated Discovery and Visualization of Communication Networks from Social Media

Growth of Social Media and Social Networks Data

Facebook

1B users

Twitter

500M users

Social Media have become an integral part of our daily lives!

Page 8: Automated Discovery and Visualization of Communication Networks from Social Media

How to Make Sense of Social Media Data?

8Anatoliy Gruzd Twitter: @dalprof

Page 9: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 10: Automated Discovery and Visualization of Communication Networks from Social Media

• 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

Page 11: Automated Discovery and Visualization of Communication Networks from Social Media

•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

Page 12: Automated Discovery and Visualization of Communication Networks from Social Media

Anatoliy Gruzd Twitter: @dalprof #1b1t Twitter Book Club

Page 13: Automated Discovery and Visualization of Communication Networks from Social Media

Anatoliy Gruzd Twitter: @dalprof 2012 Olympics in London

Page 14: Automated Discovery and Visualization of Communication Networks from Social Media

Anatoliy Gruzd Twitter: @dalprof #tarsand Twitter Community

Page 15: Automated Discovery and Visualization of Communication Networks from Social Media

•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

Page 16: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 17: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 18: Automated Discovery and Visualization of Communication Networks from Social Media

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

21Anatoliy Gruzd @dalprof

Page 19: Automated Discovery and Visualization of Communication Networks from Social Media

Automated Discovery of Social Networks

“Many to Many” Communication

ChatMailing listservForum Comments

22Anatoliy Gruzd @dalprof

Page 20: Automated Discovery and Visualization of Communication Networks from Social Media

Automated Discovery of Social Networks Approach 1: Chain Network (Reply-to)

FROM: SamPREVIOUS POSTER: Gabriel

....

....

....

Posting

header

Content

23Anatoliy Gruzd

Page 21: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 22: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 23: Automated Discovery and Visualization of Communication Networks from Social Media

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.

27Anatoliy Gruzd @dalprof

Page 24: Automated Discovery and Visualization of Communication Networks from Social Media

•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

Page 25: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 26: Automated Discovery and Visualization of Communication Networks from Social Media

Chain Network

(less connections)

Name Network

(more connections)

Evaluating Name Networks

Example: Youtube comments

Chain Network Name Network

30Anatoliy Gruzd @dalprof

Page 27: Automated Discovery and Visualization of Communication Networks from Social Media

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.

Page 28: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 29: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 30: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 31: Automated Discovery and Visualization of Communication Networks from Social Media

Agenda

• Automated Discovery of Communication Networks from

Online Data

• Sense of Community in Online Communities

• Netlytic.org

Anatoliy Gruzd @dalprof

Page 32: Automated Discovery and Visualization of Communication Networks from Social Media

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.

Page 33: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 34: Automated Discovery and Visualization of Communication Networks from Social Media

Comments Posted by Blog Readers

Anatoliy Gruzd @dalprof

Page 35: Automated Discovery and Visualization of Communication Networks from Social Media

Changes in Social Networks over Time

Anatoliy Gruzd @dalprof

Page 36: Automated Discovery and Visualization of Communication Networks from Social Media

SNA Statistics

–the posters became more connected and more of them took a stand in a group

Anatoliy Gruzd @dalprof

Page 37: Automated Discovery and Visualization of Communication Networks from Social Media

Agenda

• Automated Discovery of Communication Networks from

Online Data

• Sense of Community in Online Communities

• Netlytic.org

Anatoliy Gruzd @dalprof

Page 38: Automated Discovery and Visualization of Communication Networks from Social Media

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

Page 39: Automated Discovery and Visualization of Communication Networks from Social Media

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.

Page 40: Automated Discovery and Visualization of Communication Networks from Social Media

Twitter search keywords:

Compromised Data

Anatoliy Gruzd @dalprof

Page 41: Automated Discovery and Visualization of Communication Networks from Social Media

Twitter search keywords:

#compdata13

Anatoliy Gruzd @dalprof

Page 42: Automated Discovery and Visualization of Communication Networks from Social Media

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