Social Media Data Collection & Network Analysis with Netlytic and R

Social Media Data Collection & Network Analysis with Netlytic and R

Anatoliy [email protected]@gruzd

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

HKBU, Hong Kong

Dec 3, 2015

Twitter: @gruzd ANATOLIY GRUZD 1

mailto:[email protected]

Research at the Social Media Lab

Presentation Slides

http://bit.ly/hk15slides


http://bit.ly/hk15slides

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 Politics, Health Care and Education


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


Credit: Nathan Lapierre


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

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

Data -> Visualizations -> Understanding



http://netlytic.org/

http://netlytic.org/

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


http://107.170.179.56/community/BioDivLibrary

http://107.170.179.56/community/BioDivLibrary

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


Geography of

Twitter Networks

How to Make Sense of Social Media Data?Example: Geo-based + Content AnalysisTracking Hate Speech on Twitter


Source: http://www.fenuxe.com/tag/geo-coded

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

14

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 26Twitter: @gruzd






ANATOLIY GRUZD 27

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 29

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 31

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 + R

Twitter: @gruzd Anatoliy Gruzd 48

Twitter hashtag:

#HongKong

Instructions at

http://bit.ly/hknet15

http://bit.ly/hknet15