Learning Analytics for the Social Media Age · Learning Analytics for the Social Media Age Studying...

Learning Analytics for the Social Media Age

Studying Online Interaction using Social Network Analysis

Anatoliy [email protected]@gruzd

Canada Research Chair in Social Media Data Stewardship Associate Professor, Ted Rogers School of ManagementDirector, Social Media LabRyerson University

Open University of Hong Kong

July 7, 2016

Twitter: @gruzd ANATOLIY GRUZD 1

mailto:[email protected]

Research at the Social Media Lab • Social Media Analytics

• Social Media Data Stewardship

• Measuring Influence on Social Media

• Online Political Engagement

• Learning Analytics

• Social Media & Health

Learning Objectives 1. Social Media Data Collection

2. Basics of Social Network Analysis

3. Visualization and Analysis of Communication Networks

4. Practice using Netlytic: Studying Collaborative Learning in cMOOC


Twitter: @gruzd

ANATOLIY GRUZD

Social Media sites have become

an integral part of our daily lives!

Growth of Social Media Data

Facebook

1.5B users

Instagram

400M users

Twitter

300M users

Decision Making

in domains such as Education, Politics, Health Care


How to Make Sense of Social Media Data?

Self-collected/reported

Public APIs

Data Resellers

How to Make Sense of Social Media Data?Big Data Technology


Cloud & Distributed Computing

Data & Information Organization

Analytics Visualization


Social Media Analytics Toolshttp://socialmedialab.ca/apps/social-media-toolkit/

http://socialmedialab.ca/apps/social-media-toolkit/

Data -> Visualizations -> Understanding



http://netlytic.org/

How to Make Sense of Social Media Data?Example: Geo-based Analysis


http://107.170.179.56/community/BioDivLibrary

Social Network Analysis (SNA)

• Nodes = People

• Edges /Ties (lines) = Relations/

“Who retweeted/ replied/

mentioned whom”



Makes it much easier to understand what is going on

in a group

Advantages of

Social Network Analysis

Once the network is discovered, we can find

out:

• How do people interact with each other,

• Who are the most/least active members,

• Who is influential in a group,

• Who is susceptible to being influenced,

etc…

Twitter: @gruzdANATOLIY GRUZD

11

Liberal

ConservativeSpam

Unknown &

Undecided

NDP

Left

Green

Bloc

Other

Gruzd, A. and Roy, J (2014). Political Polarization on Social Media: Do

Birds of a Feather Flock Together on Twitter? Policy & Internet.

Common approach for collecting social network data:

• Self-reported social network data may not be available/accurate

• Surveys or interviews

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?


Studying Online Social Networks

http://www.visualcomplexity.com/vc

Forum networks

Blog networks

Friends’ networks (Facebook,

Twitter, Google+, etc…)

Networks of like-minded people

(YouTube, Flickr, etc…)


Goal: Automated Networks Discovery

Challenge: Figuring out what content-based features of online interactions can help to uncover nodes and ties between group members

How Do We Collect Information About Online Social Networks?


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

ChatMailing listservForum Comments


@John

@Peter

@Paul • Nodes = People

• Ties = “Who retweeted/

replied/mentioned whom”

• Tie strength = The number of

retweets, replies or mentions

Automated Discovery of Social NetworksTwitter Networks



Twitter Data Examples

Network Ties

@Cheeflo -> @JoeProf@Cheeflo -> @VMosco@JoeProf -> @VMosco


Network Tie

@Gruzd -> @SidneyEve

Connection type: Mention

Connection type: Reply

Sample Twitter Searches

#ELECTION2016 #HONGKONG


3557 records (Dec 3, 2015)1394 records (Oct 29, 2015)





https://netlytic.org/network/sigma.php?c=p6UFT9JUlsWz2CHQ&viz=2&datatype=twitter





What do these visualizations tell us?


SNA MeasuresMicro-level

In-degree centrality

Out-degree centrality

Betweenness centrality

Other centrality measures (e.g., closeness, eigenvector)

Macro-level

Density

Diameter

Reciprocity

Centralization

Modularity

ANATOLIY GRUZD 23Twitter: @gruzd






ANATOLIY GRUZD 24

In-degree suggests “prestige” highlighting the most mentioned or replied Twitter users

Twitter: @gruzd

In-degree centrality#HongKong Twitter network


SEVENTEEN or SVT is

a S.Korean boy group formed

by Pledis Entertainment






ANATOLIY GRUZD 26

Out-degree reveals active Twitter users with a good awareness of others in the network

Twitter: @gruzd

Out-degree centrality#HongKong Twitter network


Note: A music fan (many

retweets & replies to others)






ANATOLIY GRUZD 28

Betweenness shows actors who are located on the most number of information paths and who often connect different groups of users in the network

Twitter: @gruzd

Betweenness centrality#HongKong Twitter network


Note: A fan (retweets/replies to messages

from two different fan communities/sites)

SNA MeasuresMacro-level

Density

Diameter

Reciprocity

Centralization

Modularity

Density indicates the overall connectivity in the network (the total number of connections divided by the total number of possible connections).

It is equal to 1 when everyone is connected to everyone.


User1 User3

User2Density = 1

#Election2016 #HongKong

Nodes 491 2570

Edges 1075 2447

Density 0.005 (0.5%) 0.0004 (0.04%)

Diameter

Reciprocity

Centralization

Modularity



Density

Diameter

Reciprocity

Centralization

Modularity

Diameter gives a general idea of how “wide” the network is; the longest of the shortest paths between any two nodes in the network.


#1

User1User3

User2

User4

Diameter = 3

#2

#3


Nodes 491 2570

Edges 1075 2447

Density 0.005 (0.5%) 0.0004 (0.04%)

Diameter 28 14

Reciprocity

Centralization

Modularity



Density

Diameter

Reciprocity

Centralization

Modularity

Reciprocity shows how many online participants are having two-way conversations.

In a scenario when everyone replies to everyone, the reciprocity value will be 1.


User2

User1User3

User4 Reciprocity=1


Nodes 491 2570

Edges 1075 2447

Density 0.005 (0.5%) 0.0004 (0.04%)

Diameter 28 14

Reciprocity 0.006 (0.6%) 0.003 (0.3%)

Centralization

Modularity



Density

Diameter

Reciprocity

Centralization

Modularity

Centralization indicates whether a network is dominated by few central participants (values are closer to 1),

or whether more people are contributing to discussion and information dissemination (values are closer to 0).


User2

User1User3

User4 Centralization=1


Nodes 491 2570

Edges 1075 2447

Density 0.005 (0.5%) 0.0004 (0.04%)

Diameter 28 14

Reciprocity 0.006 (0.6%) 0.003 (0.3%)

Centralization 0.05 0.11

Modularity



Density

Diameter

Reciprocity

Centralization

Modularity

Modularity provides an estimate of whether a network consists of one coherent group of participants who are engaged in the same conversation and who are paying attention to each other (values closer to 0);

or whether a network consists of different conversations and communities with a weak overlap (values closer to 1).



Nodes 491 2570

Edges 1075 2447

Density 0.005 (0.5%) 0.0004 (0.04%)

Diameter 28 14

Reciprocity 0.006 (0.6%) 0.003 (0.3%)

Centralization 0.05 0.11

Modularity 0.42 0.92


Practice with Netlytic.orgStudying Collaborative Learning in cMOOC

Tutorial Steps :

https://netlytic.org/home/?p=470

Also see Gruzd, A., Paulin, D., & Haythornthwaite, C. (forthcoming). Analyzing Social Media and Learning Through Content and Social Network Analysis: A Faceted Methodological Approach. Journal of Learning Analytics.


https://netlytic.org/home/?p=470

43

cMOOC Dataset

Athabasca University Course◦ CCK11: Connectivism and Connective Knowledge

Not restricted to any one

platform

“Throughout this ‘course’

participants will use a variety of

technologies, for example, blogs,

Second Life, RSS Readers,

UStream, etc.”

ANATOLIY GRUZD @gruzd

44

CCK11 MOOC Dataset: Sample course page


45

CCK11 MOOC Dataset

#Posts Avg charactercount

Avg word count

Blogs 812 332.3 54.2

DiscussionThreads

68 541.8 90.8

Comments 306 837.6 138.9

Twitter posts 1722 113.9 14.4


Today’s

focus

Question 1 - #LAK14 #pLASMAWhat does this visualization tell us from an instructor’s perspective?

ANATOLIY GRUZD 46@gruzd

https://netlytic.org/gephi/sigma.php?c=OLAl80RsWu44oOlh&viz=2

What does this visualization tell us from a student’s perspective?



What does this visualization not tell us?



SNA Measures (recap)Micro-level





Macro-level

Density

Diameter

Reciprocity

Centralization

Modularity







ANATOLIY GRUZD 50

In-degree suggests “prestige” highlighting the most mentioned or replied Twitter users

@gruzd

51

#CCK11 Class Tweets (Node size=“Indegree”)








ANATOLIY GRUZD 52

Out-degree reveals active Twitter users with a good awareness of others in the network

@gruzd

53

#CCK11 Class Tweets (Node size=“Outdegree”)



Top 10 Twitter users in the Twitter network based on centrality measures


IN-DEGREE OUT-DEGREE

participant1(m) cck11feeds

participant2(f) participant8(m)

gsiemens web20education

downes participant9(f)

guestLecturer3(m) participant6(m)

web20education participant1(m)

participant4(f) participant10(f)

participant5(m) participant4(f)

participant6(m) participant7(m)

participant7(m) participant11(f)

participant8(m)


Density

Diameter

Reciprocity

Centralization

Modularity

Density indicates the overall connectivity in the network (the total number of connections divided by the total number of possible connections).

It is equal to 1 when everyone is connected to everyone.


Student

InstructorStudent

Density = 1

#CCK11 Class #Election2016 #HongKong

Nodes 498 491 2570

Edges 761 1075 2447

Density 0.003 (0.3%) 0.005 (0.5%) 0.0004 (0.04%)




Density

Diameter

Reciprocity

Centralization

Modularity

Diameter gives a general idea of how “wide” the network is; the longest of the shortest paths between any two nodes in the network.


#1

Student A Instructor

Student B

Student C

Diameter = 3

#2

#3


Nodes 498 491 2570

Edges 761 1075 2447

Density 0.003 (0.3%) 0.005 (0.5%) 0.0004 (0.04%)

Diameter 38 28 14




Density

Diameter

Reciprocity

Centralization

Modularity

Reciprocity shows how many online participants are having two-way conversations.

In a scenario when everyone replies to everyone, the reciprocity value will be 1.


Student

InstructorStudent

Student Reciprocity=1


Nodes 498 491 2570

Edges 761 1075 2447

Density 0.003 (0.3%) 0.005 (0.5%) 0.0004 (0.04%)

Diameter 38 28 14

Reciprocity 0.09 (9%) 0.006 (0.6%) 0.003 (0.3%)




Density

Diameter

Reciprocity

Centralization

Modularity

Centralization indicates whether a network is dominated by few central participants (values are closer to 1),

or whether more people are contributing to discussion and information dissemination (values are closer to 0).


Student

InstructorStudent

Student Centralization=1


Nodes 498 491 2570

Edges 761 1075 2447

Density 0.003 (0.3%) 0.005 (0.5%) 0.0004 (0.04%)

Diameter 38 28 14

Reciprocity 0.09 (9%) 0.006 (0.6%) 0.003 (0.3%)

Centralization 0.07 0.05 0.11




Density

Diameter

Reciprocity

Centralization

Modularity

Modularity provides an estimate of whether a network consists of one coherent group of participants who are engaged in the same conversation and who are paying attention to each other (values closer to 0);

or whether a network consists of different conversations and communities with a weak overlap (values closer to 1).



Nodes 498 491 2570

Edges 761 1075 2447

Density 0.003 (0.3%) 0.005 (0.5%) 0.0004 (0.04%)

Diameter 38 28 14

Reciprocity 0.09 (9%) 0.006 (0.6%) 0.003 (0.3%)

Centralization 0.07 0.05 0.11

Modularity 0.67 0.42 0.92



Learning Analytics for the Social Media Age

Studying Online Interaction using Social Network Analysis

Anatoliy [email protected]@gruzd

Canada Research Chair in Social Media Data Stewardship Associate Professor, Ted Rogers School of ManagementDirector, Social Media LabRyerson University

Open University of Hong Kong

July 7, 2016


mailto:[email protected]

Learning Analytics for the Social Media Age · Learning Analytics for the Social Media Age Studying...

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