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PREM SANKAR C
Research Scholar
Dept of Futures Studies, University of Kerala
Computational Social Science
It's is all about data and connections .....
KNM Govt Arts and Science College
Agenda
1) Social Behavior in Digital World
2) Social Data Sets to Computational Models
3) Social Network Analysis 
4) Case Studies and Practical Applications
5) Open Research Problems
What is Computational Social
Science?
Cyberculture Studies Digital Dataset + Computational Methods
1. Definition : CSS 
The increasing integration of technology into our lives
has created unprecedented volumes of data on
society’s everyday behaviour. Such data opens up
exciting new opportunities to work towards a
quantitative understanding of our complex social
systems, within the realms of a new discipline known
as Computational Social Science.
(Conte at al. 2012)
Social Sciences include:
Pure fields:
Political Science
Sociology
Economics
Psychology
Applied fields
Management
Marketing
Information systems
Policy analysis
Finance
Study of how humans
organize and interact in our
modern society....
Discovery of new patterns and their quantitative description in socio-economic
systems.

Interface between computer science and the traditional social
sciences
Era of Electronic Word of Mouth Communication

Decentralized control and Self-organization - Group behavior emerges from the
local interactions of individuals (Gender Studies / Collective Actions)

#Metoo

Social relationships are hidden
ICT - Huge Volume of Data
Internet /Social media/ Data Mining
=>Computational Social Science /
Digital Humanities
Interview
Questionnaire
Survey
“Social relationships are hidden to Real World”
The Hidden Pattern Behind Everything We Do
The New Science of Networks
Secret behind Microsoft's acquisition of LinkedIn
* Microsoft acquired Nokia for $7.9 million, Skype for $8.5 billion and
Linkedin $26.2 billion
It's all about Professional Connections !!!
Computational Social Science (CSS)

CSS is a research discipline at the interface between
computer science and the traditional social sciences.

It uses computational methods to analyze and model
social phenomena, social structures, and collective
behavior.
Computer Science + Social Science

Computer Science

Study anything

Methods driven

Large data

Prediction

Social Science

Study social things

Question driven

Small data

Explanation
Feel the Difference
Summer Institute in Computational Social Science
Datasets - Online
• Stanford Large Network Dataset Collection
http://snap.stanford.edu/data/index.html
• Arizona State Social Computing Datasets
http://socialcomputing.asu.edu/pages/datasets
• Social Computing Research at MPI-SWS
http://socialnetworks.mpi-sws.org/datasets.html
• Kaggle Datasets
https://www.kaggle.com/datasets
• Yahoo Webscope Datasets
http://webscope.sandbox.yahoo.com/
http://snap.stanford.edu/data/index.html
Digital Trace Data: Examples
1) Social media sites
2) Web search queries
3) Blogs and internet forums
4) Call detail records from mobile phones
5) Sensor data
Online networks
Call logs
Instant messaging
There has been a fundamental shift in the opportunities in data
collection about humans.
Data-driven social science
Social Science in Digital Era !!!

Digital Datasets can provide information that can profoundly
shape our understanding of social phenomena.

Convert these data sets into computational models

Simulation of complex social interactions or Computational
approaches with data to estimate models of social
phenomenon.

The main computational approaches to the social sciences are
1) Social network analysis
2) Social mining
3) Social complexity modeling
4) Social simulation models.
Data is the new oil
From Data to Theory?
Sociological Theory
Hypothesis / Prediction
Web Data
We need theory, because we want to explain social issues.
CSS Methodologies

Case study analysis

Controlled experiments

Computational modeling

Integrative data analysis / natural experiments
Understanding how network
works is an essential 21 st
century literacy..
2. Social Network Analysis
We live in networks !!!
What is SNA?

Social network analysis (SNA) is a collection of techniques,
tools, and methods by which one can map and analyze the
connections across individuals or groups or institutions.

Network analysis allows us to examine how the structure of
networks.
Why SNA?
SNA is multidisciplinary and deals with
• Influencing groups (public health, propaganda, viral marketing)
• Increasing engagement with stakeholders (Management/Recommendation)
• Cool algorithms/ Analytics (math, computer science)
• Study of social behaviour (sociology, cognitive science)
• Organizational behavior (leadership, management)
Actors (Nodes/ Vertices)
Actors –are the smallest unit of a network
 Persons
 Organizations
 Countries
 Companies
 Animals
 Words
 Web pages
 Families
Relations (Link/Edge/Tie/Arc)
Kinship
mother of, wife of
 Other role-based
boss of, teacher of
friend of
Cognitive/perceptual
knows
aware of what they know
Affective
likes, trusts
 Interactions
give advice, talks to, fights
with, lends money to
sex / drugs with
Affiliations
belong to same clubs /
companies
is physically near
Two Actors are connected
by a social relationship.
Type of Relations
Relations can be
 Undirected
 Directed
 Weighted
Weight can be
Strength
Rank
Frequency
Probability
“Think Link”
 Social network
 Collaboration network
 Terrorist networks
 Economic networks
 Family Networks
 Organization networks
 Sports Networks
 Co-author Networks
A BIs related to
Patterns are
left behind
See your
interconnected
world
See the interconnected world
all about
connections
from people
to people
Network Measures…
1- Centrality Measures
Identifying people who are well positioned to influence
the network or to move information around.
Network Science
Centrality Measures
Identifying Key People
Who are the people who are best positioned to move information through
the network?
People who live in the intersection of social worlds are at higher risk of
having good ideas. –Ron Burt
Different Leaders for Lifecycle Stages
പമഖൻ / Influencer
UCINET
PAJEK
NetDraw
R - SNA Package
R - igraph Package
Python – iGraph Package
GEPHI
Neo4J
Software packages for SNA
Network Insights Don’t Require Fancy Software
3. Interesting Facts and
Theories ...
helping you see your interconnected world
It’s a small world, after all ...
“six degrees of separation—everyone in the
world is connected to everyone else through a
chain of, at most, six mutual connections.
 In Milgram’s 1967 “small world experiment”,
individuals were asked to reach a particular
target individual by passing a message along a
chain of acquaintances. For successful chains,
the average # of intermediaries needed was 6.
Director: Fred Schepisi
Writers: John Guare (play), John
Guare (screenplay)
Stars: Will Smith, Stockard
Channing, Donald Sutherland
1993
How connected are you to everyone else in
the world?
The Facebook average is 3.57.
Source - Facebook Data Science Report
Estimated average degrees of separation between all people on Facebook.
The average person is connected to every other person by an average of 3.57
steps. The majority of people have an average between 3 and 4 steps
Three Degrees of Influence
your friends’ friends’ friends
In Book Connected by
Nicholas A. Christakis
and James H. Fowler.
The Strength of Weak Ties
Weak ties are more likely to be bridges to outside networks than strong ties
(emotionally close friends and family) .
Weak ties provide access to information and resources beyond those
available in their own social circle.
New information and ideas.
Positivity bias
The Tipping Model
How to identify the small “seed” group of people who can spread a
message across an entire network for ViralMarketing .
Super-spreaders / Hubs
Image -206 SARS patients diagnosed in Singapore were traced to four super-spreaders.
Patient Zero, the physician from China, who brought the disease to Singapore.
നനിപ്പ ബബാധയുണണ്ടെനന്ന് കണണ്ടെതനിയ 18 പപരനിൽ 16 പപരരും മരണമടഞ്ഞു ഈ പതനിനബാറു പപരരും
ആദദരും മരണമടഞ്ഞ സബാബനിത്തുമബായനി ബന്ധമള്ളവരബായനിരന
Gamifying Epidemic - http://vax.herokuapp.com/game
How do you control the behaviour of a network?
Eco Chambers -How Close Are You Really?
If you are part of a group of close friends or relations, you are less able
to make strong links outside this group.
Homophily
Homophily is the tendency to connect with people with similar
characteristics (status, beliefs, etc.)
Social Networks Expands
Idea - ‘the friends of my friends are my friends’:
The probability of three people being friends with each other in a
social network
The familiar strangers we see every day on the bus and in the supermarket
PRACTICAL
APPLICATIONS ...
helping you see your interconnected world
A) Network Dynamics & Social Behavior

How do revolutions emerge without anyone expecting
them?

How did social norms about same sex marriage change
more rapidly than anyone anticipated?

Why do some social innovations take off with relative
ease, while others struggle for years without spreading?

What are the forces that control the process of social
evolution –from the fashions that we wear, to our beliefs
about religious tolerance..

We don't need emperors or even centralized institutions
to get these kinds of social phenomenon
Diffusion of behaviors on Facebook
Posts, Share, Nomination(mention in post),Volunteer (in
comments)
Selfi Culture
Diffusion of #Hoaxs #Misinformation, #FakeNews
 Each piece of Digital misinformation contributes to the
shaping of our opinions.
 Fake news make more money than real one
 Clickbait sites manufacture hoaxes to make money from
ads, while news sites publish and spread rumors and
conspiracy theories to influence public opinion.
 Eco champers exists - Demonetization fans spreads that
the Amartya Sen endorsed it, others spread that he
opposed it. He did both.
 Trust in social networks -give higher priority to more
reliable sources.
B) Social Navigation - #Collective Actions
#Kerala Floods
#Sabarimala
# Metoo
#AyodhyaCase
Idea # using information from nearest neighbors
(follow the known crowd)
* Social information reuse.
* Trust in Social Networks –
1) Charity Activities
47
Emotion on Facebook
 Classify semantic content of status updates using LIWC
 Emotion: fraction of posts with positive/negative words
Coviello et al., PLoS ONE 2014, “Detecting Emotional Contagion in Massive Social Networks”
Slides provided by Lorenzo Coviello. Thanks! Later partially modified.
48
Twitter for Migration Studies
Use streaming API filter for geo-tagged tweets from
OECD countries
Pick 3,000 users per country, get their tweets
Estimate out-migration and oversample “static”
countries
Get data for ~500K users
After activity thresholding left with ~15K
E. Zagheni, K. Garimella, I. Weber, B. State: Inferring International
and Internal Migration Patterns from Twitter Data. WWW’14
LATEST
RESEARCH
PAPERS....
http://dx.doi.org/10.1126/science.aac4420
1) Mobile Phone Data
Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. “Predicting poverty and wealth
from mobile phone metadata.” Science 350, no. 6264 (2015): 1073-1076
1) Mobile Phone Data
Jost, John T., Pablo Barbera, Richard Bonneau, Melanie Langer, Megan Metzger, Jonathan Nagler,
Joanna Sterling, and Joshua A. Tucker. “How social media facilitates political protest:
Information, motivation, and social networks.” Political psychology 39 (2018): 85-118.
2) Twitter & Political Movements
Garcia, David, and Bernard Rime. “Collective emotions and social resilience in the digital
traces after a terrorist attack.” Psychological science (2019): 0956797619831964.
3) Twitter & Social Emotions
4) Facebook and Gender Gaps
Figure: Gender gaps in internet use computed using data from Facebook
(online
model) available at www.digitalgendergaps.org
Fatehkia, Masoomali, Ridhi Kashyap, and Ingmar Weber. “Using Facebook ad data
to track the global digital gender gap.” World Development 107 (2018): 189-209.
Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and
Larry Brilliant. “Detecting influenza epidemics using search engine query data.” Nature 457, no.
7232 (2009): 1012.
5) Google Search - Flu Detection
6) UBER Data - Taxi Meters in NYC
Farber, Henry S. “Why you can’t find a taxi in the rain and other labor supply lessons from cab
drivers.” The Quarterly Journal of Economics 130, no. 4 (2015): 1975-2026.
7) Chat bots - Anti-racism bots
Munger, Kevin. 2017. “Tweetment Effects on the Tweeted: Experimentally
Reducing Racist Harassment.” Political Behavior.
OUR
RESEARCH
PROJECTS....
Project 1 # Board Interlock Network
• Interlocking director refers to the situation in which
the same person shares positions on the boards of
more than one company.
Results..
Paper : C. Prem Sankar, K. Asokan, K. Satheesh Kumar*, Exploratory Social
Network Analysis of Affiliation Networks of Indian Listed Companies Social
Networks, Social Networks, 43, 113–120 2015.

Existence of small world structure in the Indian corporate sector.

Power elight: 2.25% of the director population account for 65.5% of the total market
capitalisation.
C. Prem Sankar, K. Asokan, K. Satheesh Kumar*, Exploratory Social Network Analysis of
Affiliation Networks of Indian Listed Companies Social Networks, Social Networks, 43,
113–120 2015.
Project 2 # Cochin Metro Network
Paper : Analysis of road network of the buffer area of Kochi Metro
rail using tools of social network analysis
Project 3 # MF Investment Network
Paper : Trust based Stock Recommendation System -a
Social Network Analysis Approach
Project 5 # Twitter Networks
#kissoflove
Zonin Alessandro ,Digital
Marketing Manager in
IBM Italy
Zonin Alessandro ,Digital
Marketing Manager in
IBM Italy
A link between
User A and User B
exists if one has
mentioned the
other in a tweet
A link between User and Hashtag exists if user has mentioned a hashtag
in their tweet
Project 4 # Linguistic Network
Independence day
Speeaches
• Each word is a node and
their co-occurrence is
encoded as the edge
between them
Paper : Forecasting and Modeling Long Term Policy Change Using Semantic
Linguistic Networks :Case Study in Indian Context
Project 7 #MakeInIndia
Paper : Social Network Visualization on How ‘MakeInIndia’ Made Vibes Among
Various Sectors – A Topic Modeling Based Approach
Studies on Bioinspired Computational Models for
Diffusion Process in Social Networks
Part1 # Influence Maximization Algorithms

In this work, We proposed a new node ranking method to measure
the influence of influencer’s in a social context by analyzing the social
interaction.
Part 2 # Agent based Simulation

In complex social systems, when traditional approaches fails to capture the dynamics,
ABMS has been reported to be successful in capturing emergent phenomena resulting
from the interactions of individual agents.

Not Exact Prediction - It provides Explanation and Experimentation
OPEN
RESEARCH
PROBLEMS....
Research Designs
1) Digital platforms & society: testing theories
2) Combining digital trace data with conventional data
sources like surveys
3) Apps for data generation and extraction
4) Combining digital traces with experiments (e.g. bots)
5) Ethics / Privacy Issues
6) Political Polarisation
7) Group Dynamics
73
Research Categories
Relational (qualitative)
• Strength of ties
• Accessibility
• Likeability/”fun”
• Reputation
• Expected reciprocity?
• Competing unit?
• Dependence
• Trust
Structural
(quantitative)
• Size
• Density
• Diversity
• Structural
Holes
• Isolates/Clique
s
• Centrality
• Betweeness
• Closeness
Individual (qualitative)
• Personality (e.g., Big
5, self-monitoring)
• Emotional
intelligence
• Intentionality
• Past experience
• Sentimental analyis
Ethics / Privacy
Computational social science often involves 
ethical/privacy questions that are now considered 
complex.
#Driverless cars  #Election  #Manorama Algorithm 
Social Scientists   Data Scientists←→
Profiling by Association
Imagine that
You’re on Facebook
Your profile is empty
But you have friends in Facebook
All PhD students
All based in Kerala (Locality / School)
Mostly from one city or one college
Are you “anonymous”?
Really hard to anonymize network information!
So, Do your PhD / Research
77
Thank You
Networks is everywhere !!!
PREM SANKAR C
prem@keralauniversity.ac.in
Ph: 8301914006

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Introduction to Computational Social Science

  • 1. PREM SANKAR C Research Scholar Dept of Futures Studies, University of Kerala Computational Social Science It's is all about data and connections ..... KNM Govt Arts and Science College
  • 3. What is Computational Social Science? Cyberculture Studies Digital Dataset + Computational Methods
  • 4. 1. Definition : CSS  The increasing integration of technology into our lives has created unprecedented volumes of data on society’s everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. (Conte at al. 2012)
  • 5. Social Sciences include: Pure fields: Political Science Sociology Economics Psychology Applied fields Management Marketing Information systems Policy analysis Finance
  • 6. Study of how humans organize and interact in our modern society.... Discovery of new patterns and their quantitative description in socio-economic systems.  Interface between computer science and the traditional social sciences
  • 7. Era of Electronic Word of Mouth Communication  Decentralized control and Self-organization - Group behavior emerges from the local interactions of individuals (Gender Studies / Collective Actions)  #Metoo  Social relationships are hidden ICT - Huge Volume of Data Internet /Social media/ Data Mining =>Computational Social Science / Digital Humanities Interview Questionnaire Survey
  • 8. “Social relationships are hidden to Real World” The Hidden Pattern Behind Everything We Do The New Science of Networks
  • 9. Secret behind Microsoft's acquisition of LinkedIn * Microsoft acquired Nokia for $7.9 million, Skype for $8.5 billion and Linkedin $26.2 billion It's all about Professional Connections !!!
  • 10. Computational Social Science (CSS)  CSS is a research discipline at the interface between computer science and the traditional social sciences.  It uses computational methods to analyze and model social phenomena, social structures, and collective behavior.
  • 11. Computer Science + Social Science  Computer Science  Study anything  Methods driven  Large data  Prediction  Social Science  Study social things  Question driven  Small data  Explanation Feel the Difference
  • 12. Summer Institute in Computational Social Science
  • 13. Datasets - Online • Stanford Large Network Dataset Collection http://snap.stanford.edu/data/index.html • Arizona State Social Computing Datasets http://socialcomputing.asu.edu/pages/datasets • Social Computing Research at MPI-SWS http://socialnetworks.mpi-sws.org/datasets.html • Kaggle Datasets https://www.kaggle.com/datasets • Yahoo Webscope Datasets http://webscope.sandbox.yahoo.com/
  • 15. Digital Trace Data: Examples 1) Social media sites 2) Web search queries 3) Blogs and internet forums 4) Call detail records from mobile phones 5) Sensor data Online networks Call logs Instant messaging There has been a fundamental shift in the opportunities in data collection about humans. Data-driven social science
  • 16. Social Science in Digital Era !!!  Digital Datasets can provide information that can profoundly shape our understanding of social phenomena.  Convert these data sets into computational models  Simulation of complex social interactions or Computational approaches with data to estimate models of social phenomenon.  The main computational approaches to the social sciences are 1) Social network analysis 2) Social mining 3) Social complexity modeling 4) Social simulation models. Data is the new oil
  • 17. From Data to Theory? Sociological Theory Hypothesis / Prediction Web Data We need theory, because we want to explain social issues.
  • 18. CSS Methodologies  Case study analysis  Controlled experiments  Computational modeling  Integrative data analysis / natural experiments
  • 19. Understanding how network works is an essential 21 st century literacy.. 2. Social Network Analysis We live in networks !!!
  • 20. What is SNA?  Social network analysis (SNA) is a collection of techniques, tools, and methods by which one can map and analyze the connections across individuals or groups or institutions.  Network analysis allows us to examine how the structure of networks. Why SNA? SNA is multidisciplinary and deals with • Influencing groups (public health, propaganda, viral marketing) • Increasing engagement with stakeholders (Management/Recommendation) • Cool algorithms/ Analytics (math, computer science) • Study of social behaviour (sociology, cognitive science) • Organizational behavior (leadership, management)
  • 21. Actors (Nodes/ Vertices) Actors –are the smallest unit of a network  Persons  Organizations  Countries  Companies  Animals  Words  Web pages  Families
  • 22. Relations (Link/Edge/Tie/Arc) Kinship mother of, wife of  Other role-based boss of, teacher of friend of Cognitive/perceptual knows aware of what they know Affective likes, trusts  Interactions give advice, talks to, fights with, lends money to sex / drugs with Affiliations belong to same clubs / companies is physically near Two Actors are connected by a social relationship.
  • 23. Type of Relations Relations can be  Undirected  Directed  Weighted Weight can be Strength Rank Frequency Probability
  • 24. “Think Link”  Social network  Collaboration network  Terrorist networks  Economic networks  Family Networks  Organization networks  Sports Networks  Co-author Networks A BIs related to Patterns are left behind See your interconnected world
  • 25. See the interconnected world all about connections from people to people
  • 26. Network Measures… 1- Centrality Measures Identifying people who are well positioned to influence the network or to move information around. Network Science
  • 28. Identifying Key People Who are the people who are best positioned to move information through the network? People who live in the intersection of social worlds are at higher risk of having good ideas. –Ron Burt
  • 29. Different Leaders for Lifecycle Stages പമഖൻ / Influencer
  • 30. UCINET PAJEK NetDraw R - SNA Package R - igraph Package Python – iGraph Package GEPHI Neo4J Software packages for SNA Network Insights Don’t Require Fancy Software
  • 31. 3. Interesting Facts and Theories ... helping you see your interconnected world
  • 32. It’s a small world, after all ... “six degrees of separation—everyone in the world is connected to everyone else through a chain of, at most, six mutual connections.  In Milgram’s 1967 “small world experiment”, individuals were asked to reach a particular target individual by passing a message along a chain of acquaintances. For successful chains, the average # of intermediaries needed was 6. Director: Fred Schepisi Writers: John Guare (play), John Guare (screenplay) Stars: Will Smith, Stockard Channing, Donald Sutherland 1993
  • 33. How connected are you to everyone else in the world? The Facebook average is 3.57. Source - Facebook Data Science Report Estimated average degrees of separation between all people on Facebook. The average person is connected to every other person by an average of 3.57 steps. The majority of people have an average between 3 and 4 steps
  • 34. Three Degrees of Influence your friends’ friends’ friends In Book Connected by Nicholas A. Christakis and James H. Fowler.
  • 35. The Strength of Weak Ties Weak ties are more likely to be bridges to outside networks than strong ties (emotionally close friends and family) . Weak ties provide access to information and resources beyond those available in their own social circle. New information and ideas.
  • 37. The Tipping Model How to identify the small “seed” group of people who can spread a message across an entire network for ViralMarketing .
  • 38. Super-spreaders / Hubs Image -206 SARS patients diagnosed in Singapore were traced to four super-spreaders. Patient Zero, the physician from China, who brought the disease to Singapore. നനിപ്പ ബബാധയുണണ്ടെനന്ന് കണണ്ടെതനിയ 18 പപരനിൽ 16 പപരരും മരണമടഞ്ഞു ഈ പതനിനബാറു പപരരും ആദദരും മരണമടഞ്ഞ സബാബനിത്തുമബായനി ബന്ധമള്ളവരബായനിരന Gamifying Epidemic - http://vax.herokuapp.com/game How do you control the behaviour of a network?
  • 39. Eco Chambers -How Close Are You Really? If you are part of a group of close friends or relations, you are less able to make strong links outside this group.
  • 40. Homophily Homophily is the tendency to connect with people with similar characteristics (status, beliefs, etc.)
  • 41. Social Networks Expands Idea - ‘the friends of my friends are my friends’: The probability of three people being friends with each other in a social network The familiar strangers we see every day on the bus and in the supermarket
  • 43. A) Network Dynamics & Social Behavior  How do revolutions emerge without anyone expecting them?  How did social norms about same sex marriage change more rapidly than anyone anticipated?  Why do some social innovations take off with relative ease, while others struggle for years without spreading?  What are the forces that control the process of social evolution –from the fashions that we wear, to our beliefs about religious tolerance..  We don't need emperors or even centralized institutions to get these kinds of social phenomenon
  • 44. Diffusion of behaviors on Facebook Posts, Share, Nomination(mention in post),Volunteer (in comments) Selfi Culture
  • 45. Diffusion of #Hoaxs #Misinformation, #FakeNews  Each piece of Digital misinformation contributes to the shaping of our opinions.  Fake news make more money than real one  Clickbait sites manufacture hoaxes to make money from ads, while news sites publish and spread rumors and conspiracy theories to influence public opinion.  Eco champers exists - Demonetization fans spreads that the Amartya Sen endorsed it, others spread that he opposed it. He did both.  Trust in social networks -give higher priority to more reliable sources.
  • 46. B) Social Navigation - #Collective Actions #Kerala Floods #Sabarimala # Metoo #AyodhyaCase Idea # using information from nearest neighbors (follow the known crowd) * Social information reuse. * Trust in Social Networks – 1) Charity Activities
  • 47. 47 Emotion on Facebook  Classify semantic content of status updates using LIWC  Emotion: fraction of posts with positive/negative words Coviello et al., PLoS ONE 2014, “Detecting Emotional Contagion in Massive Social Networks” Slides provided by Lorenzo Coviello. Thanks! Later partially modified.
  • 48. 48 Twitter for Migration Studies Use streaming API filter for geo-tagged tweets from OECD countries Pick 3,000 users per country, get their tweets Estimate out-migration and oversample “static” countries Get data for ~500K users After activity thresholding left with ~15K E. Zagheni, K. Garimella, I. Weber, B. State: Inferring International and Internal Migration Patterns from Twitter Data. WWW’14
  • 50.
  • 52. Blumenstock, Joshua, Gabriel Cadamuro, and Robert On. “Predicting poverty and wealth from mobile phone metadata.” Science 350, no. 6264 (2015): 1073-1076 1) Mobile Phone Data
  • 53. Jost, John T., Pablo Barbera, Richard Bonneau, Melanie Langer, Megan Metzger, Jonathan Nagler, Joanna Sterling, and Joshua A. Tucker. “How social media facilitates political protest: Information, motivation, and social networks.” Political psychology 39 (2018): 85-118. 2) Twitter & Political Movements
  • 54. Garcia, David, and Bernard Rime. “Collective emotions and social resilience in the digital traces after a terrorist attack.” Psychological science (2019): 0956797619831964. 3) Twitter & Social Emotions
  • 55. 4) Facebook and Gender Gaps Figure: Gender gaps in internet use computed using data from Facebook (online model) available at www.digitalgendergaps.org Fatehkia, Masoomali, Ridhi Kashyap, and Ingmar Weber. “Using Facebook ad data to track the global digital gender gap.” World Development 107 (2018): 189-209.
  • 56. Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. “Detecting influenza epidemics using search engine query data.” Nature 457, no. 7232 (2009): 1012. 5) Google Search - Flu Detection
  • 57. 6) UBER Data - Taxi Meters in NYC Farber, Henry S. “Why you can’t find a taxi in the rain and other labor supply lessons from cab drivers.” The Quarterly Journal of Economics 130, no. 4 (2015): 1975-2026.
  • 58. 7) Chat bots - Anti-racism bots Munger, Kevin. 2017. “Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment.” Political Behavior.
  • 60. Project 1 # Board Interlock Network • Interlocking director refers to the situation in which the same person shares positions on the boards of more than one company.
  • 61. Results.. Paper : C. Prem Sankar, K. Asokan, K. Satheesh Kumar*, Exploratory Social Network Analysis of Affiliation Networks of Indian Listed Companies Social Networks, Social Networks, 43, 113–120 2015.  Existence of small world structure in the Indian corporate sector.  Power elight: 2.25% of the director population account for 65.5% of the total market capitalisation.
  • 62. C. Prem Sankar, K. Asokan, K. Satheesh Kumar*, Exploratory Social Network Analysis of Affiliation Networks of Indian Listed Companies Social Networks, Social Networks, 43, 113–120 2015.
  • 63. Project 2 # Cochin Metro Network Paper : Analysis of road network of the buffer area of Kochi Metro rail using tools of social network analysis
  • 64. Project 3 # MF Investment Network Paper : Trust based Stock Recommendation System -a Social Network Analysis Approach
  • 65. Project 5 # Twitter Networks #kissoflove Zonin Alessandro ,Digital Marketing Manager in IBM Italy Zonin Alessandro ,Digital Marketing Manager in IBM Italy A link between User A and User B exists if one has mentioned the other in a tweet A link between User and Hashtag exists if user has mentioned a hashtag in their tweet
  • 66. Project 4 # Linguistic Network Independence day Speeaches • Each word is a node and their co-occurrence is encoded as the edge between them Paper : Forecasting and Modeling Long Term Policy Change Using Semantic Linguistic Networks :Case Study in Indian Context
  • 67. Project 7 #MakeInIndia Paper : Social Network Visualization on How ‘MakeInIndia’ Made Vibes Among Various Sectors – A Topic Modeling Based Approach
  • 68. Studies on Bioinspired Computational Models for Diffusion Process in Social Networks
  • 69. Part1 # Influence Maximization Algorithms  In this work, We proposed a new node ranking method to measure the influence of influencer’s in a social context by analyzing the social interaction.
  • 70. Part 2 # Agent based Simulation  In complex social systems, when traditional approaches fails to capture the dynamics, ABMS has been reported to be successful in capturing emergent phenomena resulting from the interactions of individual agents.  Not Exact Prediction - It provides Explanation and Experimentation
  • 72. Research Designs 1) Digital platforms & society: testing theories 2) Combining digital trace data with conventional data sources like surveys 3) Apps for data generation and extraction 4) Combining digital traces with experiments (e.g. bots) 5) Ethics / Privacy Issues 6) Political Polarisation 7) Group Dynamics
  • 73. 73 Research Categories Relational (qualitative) • Strength of ties • Accessibility • Likeability/”fun” • Reputation • Expected reciprocity? • Competing unit? • Dependence • Trust Structural (quantitative) • Size • Density • Diversity • Structural Holes • Isolates/Clique s • Centrality • Betweeness • Closeness Individual (qualitative) • Personality (e.g., Big 5, self-monitoring) • Emotional intelligence • Intentionality • Past experience • Sentimental analyis
  • 75. Profiling by Association Imagine that You’re on Facebook Your profile is empty But you have friends in Facebook All PhD students All based in Kerala (Locality / School) Mostly from one city or one college Are you “anonymous”? Really hard to anonymize network information!
  • 76. So, Do your PhD / Research
  • 77. 77 Thank You Networks is everywhere !!! PREM SANKAR C prem@keralauniversity.ac.in Ph: 8301914006