Automated Discovery and Visualization of Online Social Networks · Automated Discovery and...

Automated Discovery and Visualization of

Online Social Networks

Anatoliy Gruzd @dalprof [email protected] Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University

Summer School “Social Network Analysis: Internet Research”, St.Pete, Rus, 2013

Dalhousie University

Faculty of Management

School of Information Management

Social Media Lab

Social Media Lab

SocialMediaLab.ca

http://SocialMediaAndSociety.com/

http://socialmediaandsociety.com/



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

http://bit.ly/cfpInfluence

SocialMediaLab.ca Twitter: dalprof

Special Issue on Measuring Influence in Social Media

Editors: Anatoliy Gruzd, School of Information Management, Dalhousie University Barry Wellman, Department of Sociology, University of Toronto



Agenda

• Thursday, Aug 16, 10:00-11:30

Automated Discovery of Social Networks from Online Data

(Part 1): Online Forums

• Friday, Aug 17, 10:00-11:30


(Part 2): Blogs and MicroBlogs

• Wednesday, Aug 21, 10:00-11:30

Sense of Community in Online Communities

• Thursday, Aug 22, 10:00-11:30

Practice with Netlytic.org


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!

How to Make Sense of Social Media Data?

17 Anatoliy Gruzd Twitter: @dalprof

How to Make Sense of Social Media Data?

Social Network Analysis (SNA)

• Nodes = Group Members/People

• Edges /Ties (lines) =

relations / Connections


•Users

–More useful recommendation systems

• Amazon, Netflix

–A more secured/easy way to share private content with trusted individuals

• “Web of Trust” (Golbeck, 2008; Matsuo et.al., 2004)

–Improve users’ experience with information systems

• Keeping in touch with friends and colleagues (e.g., LinkedIn, Facebook)

• New browsing capabilities for news stories (Pouliquen et al, 2007; Tanev, 2007)

Why Do We Want To Discover Online Social Networks?

22 Anatoliy Gruzd @dalprof

•Companies

–Recruiting talents

•Different ties for different needs (Leung, 2003)

–Finding experts

•Expertise oriented searching using social networks (Ehrlich et al, 2007; Li et al,

2007)

–Marketing

•Viral marketing (Domingos, 2005)

•Building brand loyalty using customer networks (Thompson & Sinha, 2008)



• 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

•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?



•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]


LimeSurvey (open source) – www.limesurvey.org


VENNMAKER http://www.vennmaker.com/en


Using VennMaker to Collect an Ego Network


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?


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



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



“Many to Many” Communication

Chat Mailing listserv Forum Comments


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

FROM: Sam PREVIOUS 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 39 Anatoliy Gruzd

40

Chain Networks: missed info.

FROM: Eva REFERENCE CHAIN: Gabriel, Sam, Gina “ Gina, I owe you a cookie. This is exactly what I wanted to know. I was already planning on taking 402 next semester, and now I have something to look forward to! ”

FROM: Fred

“ I wonder if that could be why other libraries

around the world have resisted changing –

it's too much work, and as Dan pointed out, too expensive. ”

Ex.2

Ex.3

Research Question

What content-based features of online interactions can help to uncover nodes and ties between group members?




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.


•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)


Approach 2: Name Network


Summary

1. Why Do We Want To Discover Online Social Networks?

2. How Do We Collect Information About Social Networks?

– Self-reported vs Observed , Manual vs. Automated

3. Automated Discovery of Social Networks from Online

Conversations

– Methods: Chain Networks vs Name Networks

4. Evaluation of Name Networks with Forum Data



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


Chain Network

(less connections)

Name Network

(more connections)

Evaluating Name Networks

Example: Youtube comments

Chain Network Name Network


Evaluating Name Networks Results from Online Learners Dataset

Dataset

Classes 6

School year Spring 2008

Duration of each

class 15 weeks

No. of students

per class 17 – 29

Data source

• Bulletin board

messages

• Online

questionnaire

51

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.

Evaluating Name Networks

52

Name Network Chain Network

Forum Postings

Self-Reported Network

Survey Comparison Procedure:

• QAP correlations • Exponential random graph models • Manual exploration using network visualization

vs.

vs. vs.

Results

• Name networks provide on average 40% more information about social ties in a group as compared to Chain networks

53

“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 Network QAP correlation ~ 0.5

Results

• Self-reported networks are almost twice as likely to share the same ties

with Name networks than with Chain networks.

54

Chain Network Name Network

Self-Reported Network

Results

• The following social relations were found by the “name network” method

55

• 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

Learn

• ‘Learn’ relation is often discovered in postings that refer

to somebody else who has presented or posted

something awhile ago or via different communication

channel

– “… it made me think of [an example] that Karen posted ”

56

Collaborative Work

• Organizing group work, taking a leadership role

– “ Some quick poking around shows that Steve and myself are here in Champaign, [...] and Nicole is in Chicago. [...] does anyone have a strong desire to be our contact person to the administrators ”

• A reference to an event or interaction that happened outside the online forum

– “ Anne and I have been corresponding via e-mail and she reminded me that we should be having discussion here "

• A reference to the whole group or posting on behalf of the whole group

– “ Steve and Natasha, I couldn't wait to see your site. I knew it was going to [be] awesome! ”

57

Help

• ‘Help’ relation is often discovered in

– postings from students asking for the instructor’s help or

– postings that mention classmate’s name in the context of words like “thank you”, “help”, “assistance”.

58

Help

• Ask the instructor about something

– “ [Instructor’s name] if you see this posting would you please clarify for us ”

• Ask peers to clarify something that the instructor said during the lecture

– “ I remember [Instructor’s name] asking us to email her with topics [...] I wonder if that is in replacement of our bb question? ”

59

Agenda

• Thursday, Aug 16, 10:00-11:30



• Friday, Aug 17, 10:00-11:30


(Part 2): Blogs

• Wednesday, Aug 21, 10:00-11:30


• Thursday, Aug 22, 10:00-11:30



Options for Automated Discovery of Communication Networks among Blogs

Furukawa, et.al. (2007),

Ali-Hasan & Adamic (2007):

– Blogroll links

– Citation links

– Comment links

– Trackback

65




– Blogroll links

– Citation links

– Comment links

– Trackback

66




– Blogroll links

– Citation links

– Comment links

– Trackback

67




– Blogroll links

– Citation links

– Comment links

– Trackback

68

Discovering Networks in Blogosphere Example: Political Blogs

http://presidentialwatch08.com

Gruzd, A., Black, F.A., Le, Y., Amos, K. (2012). Investigating Biomedical Research Literature in the Blogosphere: A Case Study of Diabetes and HbA1c. Journal of the Medical Library Association 100(1): 34-42. DOI: 10.3163/1536-5050.100.1.007

http://dx.crossref.org/10.3163/1536-5050.100.1.007




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.

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


Comments Posted by Blog Readers


Changes in Social Networks over Time


SNA Statistics

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


Agenda

• Thursday, Aug 16, 10:00-11:30



• Friday, Aug 17, 10:00-11:30


(Part 2): Blogs

• Wednesday, Aug 21, 10:00-11:30


• Thursday, Aug 22, 10:00-11:30



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

Anatoliy Gruzd Twitter: @dalprof 2012 Olympics in London

Anatoliy Gruzd Twitter: @dalprof #tarsand Twitter Community

A Case Study of @barrywellman’s

“Imagined Community” on Twitter

Gruzd, A., Wellman, B., and Takhteyev, Y. (2011). Imagining Twitter as an Imagined Community. American Behavioral Scientist 55 (10): 1294-1318, DOI: 10.1177/0002764211409378.

Characteristics of Online Community

• Anderson’s imagined communities (1983)

– Common language

– Temporality

– High Centers

• Jones` Virtual Settlement (1997)

– virtual common-public-place

– interactivity

– sustained membership

• McMillan & Chavis` Sense of Community (1986)

– feelings of membership & influence

– reinforcement of needs

– shared emotional connection

– Wellman’s “networked individualism”

Dataset

1) August 2009

- 56 mutuals, 140 connections

2) February 2010

- 72 mutuals, 285 connections

3) April 2009 - February 2010

- 3,112 tweets

Twitterspeak: Specialized Language &

Norms

For all Twitter users:

URL shorteners to save space: bit.ly

Hashtags (#): #Sunbelt

RT (ReTweet)

@name (@dalprof)

-------------------------------

For Barry`s network:

“Wellman:” or “Me:”

“(X of Y)” or “(X/Y)”

“High Centers”

• An “imagined” community on Twitter is dual-faceted -

collective and personal

• The collective Twitter community forms around high

centers who are popular individuals (danah boyd),

celebrities (Britney), or organizations such as media

companies (BBC)

• The high centers in the personal Twitter don't have

to be “celebrities”, but

• The local and overall high centers overlap to some

extent

Interactivity

• 60% of Barry's tweets included the @ sign

• The average Twitter user – 24.5% (Huberman

et al, 2009), 12.5% (Java et al., 2006)

• “Name network” (mentions and co-occurrence of

usernames)

• 3,112 tweets-> 512 users(1,448 ties)

vs. 56 mutuals(101 ties)

• Result: The mutual network of 56 users

is 6 times denser than the larger interaction

network of 512 users

•QAP correlation of 0.27 (p<0.05) between the mutual and interaction networks.

“Manual” Clusters in Barry’s Twitter Network

Summary (1)

Barry`s network exhibits characteristics of

• Anderson’s “imagined communities”

• Jones’ “virtual settlement”

• McMillan and Chavis’ “sense of community”

• Wellman’s “networked individualism”

• Barry`s network is both “real” and “imagined”

– real because the participants interact, especially the

mutuals

– imagined because they have some sense of

community

Summary (2)

• Why Barry’s online community has grown

while maintaining a sense of community?

– Core members who actively interact with each other

& participate in the community for a long time

– Barry's community is open to newcomers

•Twitter’s asymmetric connections

•Trust, professionalism and informality

among the active mutuals

• Combo of strong & weak ties

connectivity between social circles

Case Study: #hcsmca Twitter Community

hcsmca: Health Care Social Media Canada

Haythornthwaite,C. and Gruzd, A. (forthcoming). Enabling Community through Social Media. Journal of Medical Internet

Research

Background

• #hcsmca is a vibrant community of people interested in exploring social innovation in health care. We share and learn, and together we are making health care more open and connected

• #hcsmca hosts a tweet chat every Wednesday at 1 pm ET. The last Wednesday of the month is our monthly evening chat at 9 pm ET.

Source: http://cyhealthcommunications.wordpress.com/hcsmca-2/

http://cyhealthcommunications.wordpress.com/hcsmca-2/



Research questions

1. What accounts for the relative longevity of this particular online community?

– Is it because of the founder’s leadership and her continuing involvement in this community?

– Or is there a core group of members who are also actively and persistently involved in this community?

2. What is the composition of this community? Does one’s professional role/title determine a person’s centrality within this community.

Step 1: Data Collection Data: Public Twitter messages that mentioned the #hcsmca hashtag/keyword Collection Period: November 12 – December 13, 2012 Software: Netlytic http://netlytic.org

http://netlytic.org/

Topics Covered (1)

Nov 14, 2012 T1: Challenge of engaging SM to inform a research agenda

T2: Use of innovation, SM, and gamification to encourage

uptake of self-care

Topics Covered (2) Nov 21, 2012 T1 Healthcare blogs should we or shouldn’t we, what have

we learned, what are the benefits?

T2 Are healthcare blogs a useful tool for education and

knowledge transfer?

Topics Covered (3)

Nov 28, 2012 T1: How has social media made you healthier? Unhealthier?

Has social media made our health choices more numerous

and this overwhelming?

T2: What messaging would motivate you to make a positive

health change? Who would you listen to?

Step 2: Discover #hcsmca Communication Network

“Name Networks” Tie = Who mentions or replies to whom

Automated Discovery of Online Social Networks

Example: Tweets

@John

@Peter

@Paul

• Nodes = People

• Ties = “Who retweeted/

replied/mentioned whom”

• Tie strength = The number of

retweets, replies or mentions


Step 3: Social Network Analysis using ORA and Ucinet

#hcsmca Communication Network on Twitter (Nov 12 - Dec 13)

Net viz in Netlytic: http://netlytic.org/gephi/sigma.php?c=0ZnbSm6D23u07bT0&viz=2

http://netlytic.org/gephi/sigma.php?c=0ZnbSm6D23u07bT0&viz=2







#hcsmca Communication Network on Twitter (Nov 12 - Dec 13)

*Roles are assigned manually

Roles Count

SM health content

providers 110

Unaffiliated individual users 89

Communicators - not

specifically health related 74

Communicators - Health

related 59

Healthcare professionals 50

Health institutions 31

Advocacy 30

Students 16

Educators, professors 13

Researchers 10

Government and health

policy makers 4

Node size = In-Degree Centrality

#hcsmca Communication Network on Twitter

Nodes are automatically grouped based on their roles No apparent clustering among people in the same role (notice cross-group ties)

Procedure: Analysis of Variance Density Test using UCINET

Agenda

• Thursday, Aug 16, 10:00-11:30



• Friday, Aug 17, 10:00-11:30


(Part 2): Blogs

• Wednesday, Aug 21, 10:00-11:30


• Thursday, Aug 22, 10:00-11:30



Netlytic a cloud-based analytic tool for automated text analysis &

discovery of social networks from online communication

Ne

two

rk

s

Sta

ts

Co

nte

nt


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.



General Stats about Your Dataset

Interactive Tag Cloud of Top Words From

Your Dataset Example: 2012 Halifax Municipal Election Dataset


Stacked Chart of Top Words Over Time Examples: “@Tomaskformore, @teamsavagehrm, Online, etc…


All Mentions of a Particular Top Word or Concept is 1 Click Away


TreeMap of User-defined Cognitive & Social Categories Examples: “Fear, promotion, self, uncertainty, disagreement, etc…


User-defined Cognitive & Social Categories for “Disagreement” Example : “No, wrong, however, agree…but, etc…”


All Mentions of “NO” Within the User-Defined

Cognitive & Social Categories for “Disagreement”


Visualization of Communication Networks Netlytic can automatically build 2 types of network from your dataset

1. Chain Network (Who Replies-to-Whom)

2. Name Network (Who Mentions-Whom within a Message)


All Connections Between Any 2 Nodes is 1 Click Away


Anatoliy Gruzd Twitter: @dalprof

#1b1t Twitter Book Club § Gruzd, A. and Sedo, D.R. (2012) #1b1t: Investigating Reading Practices at the Turn of the Twenty-first Century.Journal of Studies in Book Culture 3(2). Available at http://id.erudit.org/iderudit/1009347ar

Sample Dataset

Automated Discovery and Visualization of

Online Social Networks

Anatoliy Gruzd @dalprof [email protected] Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University

Summer School “Social Network Analysis: Internet Research”, St.Pete, Rus, 2013

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