homework

POLI 100F Lecture 3: Social Networks and Behavior

Gregoire Phillips

University of California, San Diego

[email protected]

October 19, 2020

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 1 / 23

Overview

1 Announcements

2 Social Networks and Behavior

3 Moving to Social Network Applications

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 2 / 23

A few brief reminders

Your first Participation Quiz is now online and will be due by next Tuesday by 11:59 PM PDT

Found under Quizzes or Assignments

It’s five multiple choice questions designed to test your memory of important content

You’ll want to both watch the lecture and do the reading before attempting – you only get one attempt

Finally, your project pitch memo is online and due by Friday, Oct. 30th at 11:59 PM PDT

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 3 / 23

A few brief reminders

Your first Participation Quiz is now online and will be due by next Tuesday by 11:59 PM PDT

Found under Quizzes or Assignments

It’s five multiple choice questions designed to test your memory of important content

You’ll want to both watch the lecture and do the reading before attempting – you only get one attempt

Finally, your project pitch memo is online and due by Friday, Oct. 30th at 11:59 PM PDT

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 3 / 23

A few brief reminders

Your first Participation Quiz is now online and will be due by next Tuesday by 11:59 PM PDT

Found under Quizzes or Assignments

It’s five multiple choice questions designed to test your memory of important content

You’ll want to both watch the lecture and do the reading before attempting – you only get one attempt

Finally, your project pitch memo is online and due by Friday, Oct. 30th at 11:59 PM PDT

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 3 / 23

A few brief reminders

Your first Participation Quiz is now online and will be due by next Tuesday by 11:59 PM PDT

Found under Quizzes or Assignments

It’s five multiple choice questions designed to test your memory of important content

You’ll want to both watch the lecture and do the reading before attempting – you only get one attempt

Finally, your project pitch memo is online and due by Friday, Oct. 30th at 11:59 PM PDT

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 3 / 23

A few brief reminders

Your first Participation Quiz is now online and will be due by next Tuesday by 11:59 PM PDT

Found under Quizzes or Assignments

It’s five multiple choice questions designed to test your memory of important content

You’ll want to both watch the lecture and do the reading before attempting – you only get one attempt

Finally, your project pitch memo is online and due by Friday, Oct. 30th at 11:59 PM PDT

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 3 / 23

Today’s Framing Questions

Our questions:

What are the mechanisms through which we shape our social networks?

What are the mechanisms through which social networks shape individual and group behavior?

How do social scientists think about and measure this?

Our goal today:

Identify mechanisms through which we shape social networks, and social networks shape our behavior

Identify the contexts in which this occurs

Illustrate social science applications of thinking about and measuring this effect in the real world

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 4 / 23

Today’s Framing Questions

Our questions:

What are the mechanisms through which we shape our social networks?

What are the mechanisms through which social networks shape individual and group behavior?

How do social scientists think about and measure this?

Our goal today:

Identify mechanisms through which we shape social networks, and social networks shape our behavior

Identify the contexts in which this occurs

Illustrate social science applications of thinking about and measuring this effect in the real world

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 4 / 23

Recall: Basic Social Network Structure and Mechanics

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 5 / 23

Good Starting Point: Five Rules, from Connected

Fowler & Christakis lay out five rules of social networks:

1 We shape our network

2 Our network shapes us

3 Our friends affect us

4 Our friends’ friends’ friends affect us

5 The network has a life of its own

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 6 / 23

Good Starting Point: Five Rules, from Connected

Fowler & Christakis lay out five rules of social networks:

1 We shape our network

2 Our network shapes us

3 Our friends affect us

4 Our friends’ friends’ friends affect us

5 The network has a life of its own

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 6 / 23

Good Starting Point: Five Rules, from Connected

Fowler & Christakis lay out five rules of social networks:

1 We shape our network

2 Our network shapes us

3 Our friends affect us

4 Our friends’ friends’ friends affect us

5 The network has a life of its own

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 6 / 23

Good Starting Point: Five Rules, from Connected

Fowler & Christakis lay out five rules of social networks:

1 We shape our network

2 Our network shapes us

3 Our friends affect us

4 Our friends’ friends’ friends affect us

5 The network has a life of its own

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 6 / 23

Good Starting Point: Five Rules, from Connected

Fowler & Christakis lay out five rules of social networks:

1 We shape our network

2 Our network shapes us

3 Our friends affect us

4 Our friends’ friends’ friends affect us

5 The network has a life of its own

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 6 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

For the most part, we determine part of the structure of our networks

how many people we are connected to

the nature of these connections

the depth of these connections

the durability of these connections over time

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 7 / 23

1. We Shape Our Network

We also influence the density of interconnections between friends and family

introduce friends from seperate groups to one another

blend families in new relationships

split networks when connections are broken

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 8 / 23

1. We Shape Our Network

We also influence the density of interconnections between friends and family

introduce friends from seperate groups to one another

blend families in new relationships

split networks when connections are broken

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 8 / 23

1. We Shape Our Network

We also influence the density of interconnections between friends and family

introduce friends from seperate groups to one another

blend families in new relationships

split networks when connections are broken

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 8 / 23

1. We Shape Our Network

We also influence the density of interconnections between friends and family

introduce friends from seperate groups to one another

blend families in new relationships

split networks when connections are broken

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 8 / 23

1. We Shape Our Network

We also influence the density of interconnections between friends and family

introduce friends from seperate groups to one another

blend families in new relationships

split networks when connections are broken

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 8 / 23

1. We Shape Our Network

We also influence how central we are within a social network

make friends with everyone, or form few social connections maintain socialposition over time as network expands

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 9 / 23

1. We Shape Our Network

We also influence how central we are within a social network

make friends with everyone, or form few social connections

maintain socialposition over time as network expands

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 9 / 23

1. We Shape Our Network

We also influence how central we are within a social network

make friends with everyone, or form few social connections maintain socialposition over time as network expands

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 9 / 23

1. We Shape Our Network

We also influence how central we are within a social network

make friends with everyone, or form few social connections maintain socialposition over time as network expands

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 9 / 23

1. We Shape Our Network

We also influence how interconnected our networks are

We influence network transitivity, or the degree to which all those in a network know one another

High transitivity people are deeply embedded within a single group (person A)

Low transitivity people act as a bridge between different groups, connected with people who don’t know each other (person B)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 10 / 23

1. We Shape Our Network

We also influence how interconnected our networks are

We influence network transitivity, or the degree to which all those in a network know one another

High transitivity people are deeply embedded within a single group (person A)

Low transitivity people act as a bridge between different groups, connected with people who don’t know each other (person B)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 10 / 23

1. We Shape Our Network

We also influence how interconnected our networks are

We influence network transitivity, or the degree to which all those in a network know one another

High transitivity people are deeply embedded within a single group (person A)

Low transitivity people act as a bridge between different groups, connected with people who don’t know each other (person B)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 10 / 23

1. We Shape Our Network

We also influence how interconnected our networks are

We influence network transitivity, or the degree to which all those in a network know one another

High transitivity people are deeply embedded within a single group (person A)

Low transitivity people act as a bridge between different groups, connected with people who don’t know each other (person B)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 10 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

2. Our Network Shapes Us

Networks facilitate the spread of:

information

emotions

wealth

disease

health/unhealth (?)

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 11 / 23

3 & 4: Our Friends (and their connections) Affect Us

The process through which things spread through a network is called contagion.

Contagion describes what flows across ties in a network

May manifest as dyadic spread, or the tendency of effects to spread from one person to you directly through a tie

May also manifest as hyperdyadic spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 12 / 23

3 & 4: Our Friends (and their connections) Affect Us

The process through which things spread through a network is called contagion.

Contagion describes what flows across ties in a network

May manifest as dyadic spread, or the tendency of effects to spread from one person to you directly through a tie

May also manifest as hyperdyadic spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 12 / 23

3 & 4: Our Friends (and their connections) Affect Us

The process through which things spread through a network is called contagion.

Contagion describes what flows across ties in a network

May manifest as dyadic spread, or the tendency of effects to spread from one person to you directly through a tie

May also manifest as hyperdyadic spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 12 / 23

3 & 4: Our Friends (and their connections) Affect Us

Not just the quantity of connections, but also the quality that matters

Granovetter’s “The strength of weak ties’ describes how weak – and not strong – connections between individuals may be the most important to the spread of contagions across networks

Weak ties are the most likely to facilitate bridges within a social network between clusters of individuals unlikely to interact otherwise

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 13 / 23

3 & 4: Our Friends (and their connections) Affect Us

Not just the quantity of connections, but also the quality that matters

Granovetter’s “The strength of weak ties’ describes how weak – and not strong – connections between individuals may be the most important to the spread of contagions across networks

Weak ties are the most likely to facilitate bridges within a social network between clusters of individuals unlikely to interact otherwise

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 13 / 23

3 & 4: Our Friends (and their connections) Affect Us

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 14 / 23

Summarize: Us to Network

What are the mechanisms through which we shape our social networks?

We influence the shape of our networks through:

density of the connections we gain and maintain

our centrality within the network

the transitivity – inter-connectedness – of the connections we make and maintain

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 15 / 23

Summarize: Us to Network

What are the mechanisms through which we shape our social networks?

We influence the shape of our networks through:

density of the connections we gain and maintain

our centrality within the network

the transitivity – inter-connectedness – of the connections we make and maintain

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 15 / 23

Summarize: Us to Network

What are the mechanisms through which we shape our social networks?

We influence the shape of our networks through:

density of the connections we gain and maintain

our centrality within the network

the transitivity – inter-connectedness – of the connections we make and maintain

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 15 / 23

Summarize: Us to Network

What are the mechanisms through which we shape our social networks?

We influence the shape of our networks through:

density of the connections we gain and maintain

our centrality within the network

the transitivity – inter-connectedness – of the connections we make and maintain

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 15 / 23

Summarize: Network to Us

What are the mechanisms through which our social networks shape us?

Our social networks shape us through:

contagion – things like information, germs, money, violence, happiness that flow across ties

Sometimes occurs through dyadic spread – or the spread of something from one person to their direct social ties Sometimes occurs through hyperdyadic spread – or the spread of something from person to person to person outside of direct social ties

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 16 / 23

Summarize: Network to Us

What are the mechanisms through which our social networks shape us?

Our social networks shape us through:

contagion – things like information, germs, money, violence, happiness that flow across ties

Sometimes occurs through dyadic spread – or the spread of something from one person to their direct social ties Sometimes occurs through hyperdyadic spread – or the spread of something from person to person to person outside of direct social ties

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 16 / 23

Summarize: Network to Us

What are the mechanisms through which our social networks shape us?

Our social networks shape us through:

contagion – things like information, germs, money, violence, happiness that flow across ties

Sometimes occurs through dyadic spread – or the spread of something from one person to their direct social ties

Sometimes occurs through hyperdyadic spread – or the spread of something from person to person to person outside of direct social ties

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 16 / 23

Summarize: Network to Us

What are the mechanisms through which our social networks shape us?

Our social networks shape us through:

contagion – things like information, germs, money, violence, happiness that flow across ties

Sometimes occurs through dyadic spread – or the spread of something from one person to their direct social ties Sometimes occurs through hyperdyadic spread – or the spread of something from person to person to person outside of direct social ties

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 16 / 23

Summarize: Network to Us

What are the mechanisms through which our social networks shape us?

Our social networks shape us through:

contagion – things like information, germs, money, violence, happiness that flow across ties

Sometimes occurs through dyadic spread – or the spread of something from one person to their direct social ties Sometimes occurs through hyperdyadic spread – or the spread of something from person to person to person outside of direct social ties

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 16 / 23

Behavioral Example: Violent Crime

The way that we shape social networks can help us map and understand criminal behavior

..... better than television can.

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 17 / 23

Behavioral Example: Violent Crime

The way that we shape social networks can help us map and understand criminal behavior

..... better than television can.

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 17 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

More than 75% of homicides involve people who knew each other

In network language: individuals with ties within social network are the most likely to be responsible for extreme violence against others within the network

Of the remaining 25%, nearly 23% involve people within two degrees from one another

Very, very few homicides involve complete strangers

Violent crime, including homocide, cascades through networks

If a murder occurs within a social network, the probability that another murder will occur rises substantially

Phenomenon applies to most violent crime embedded in social networks

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 18 / 23

Behavioral Example: Violent Crime

Most homocides involve disruptions or disputes within networks

breakdown of social, financial, or romantic connections between individuals trigger violent reactions from them or their direct connections

One way to combat homocide and violent crime in places where it is systemic: plant violence disrupters within the network

In social disputes: violence mediators trained to resolve disputes between family members can be individuals within families that receive special training

In schools: “Idea leader” endorsement of anti-violence and anti-bullying campaign disrupts violent activities throughout network

Changing the composition of the network can disrupt cascades of things like violent behavior

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 19 / 23

Behavioral Example: Violent Crime

Most homocides involve disruptions or disputes within networks

breakdown of social, financial, or romantic connections between individuals trigger violent reactions from them or their direct connections

One way to combat homocide and violent crime in places where it is systemic: plant violence disrupters within the network

In social disputes: violence mediators trained to resolve disputes between family members can be individuals within families that receive special training

In schools: “Idea leader” endorsement of anti-violence and anti-bullying campaign disrupts violent activities throughout network

Changing the composition of the network can disrupt cascades of things like violent behavior

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 19 / 23

Behavioral Example: Violent Crime

Most homocides involve disruptions or disputes within networks

breakdown of social, financial, or romantic connections between individuals trigger violent reactions from them or their direct connections

One way to combat homocide and violent crime in places where it is systemic: plant violence disrupters within the network

In social disputes: violence mediators trained to resolve disputes between family members can be individuals within families that receive special training

In schools: “Idea leader” endorsement of anti-violence and anti-bullying campaign disrupts violent activities throughout network

Changing the composition of the network can disrupt cascades of things like violent behavior

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 19 / 23

Behavioral Example: Violent Crime

Most homocides involve disruptions or disputes within networks

breakdown of social, financial, or romantic connections between individuals trigger violent reactions from them or their direct connections

One way to combat homocide and violent crime in places where it is systemic: plant violence disrupters within the network

In social disputes: violence mediators trained to resolve disputes between family members can be individuals within families that receive special training

In schools: “Idea leader” endorsement of anti-violence and anti-bullying campaign disrupts violent activities throughout network

Changing the composition of the network can disrupt cascades of things like violent behavior

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 19 / 23

Behavioral Example: Public Health

The way that social networks shape behavior can tell us something about how a virus spreads

The concept of contagion is perhaps best thought about in 2020 in the spread of a virus

The structure of social networks directly facilitates the spread of an infectious disease

Viruses spread through networks in a directed path – one individual spreads a virus to another individual, and typically cannot get that virus back from the other

While the contagiousness and severity of the virus are set outside of the network, the density of the network itself can make viruses more or less likely to spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 20 / 23

Behavioral Example: Public Health

The way that social networks shape behavior can tell us something about how a virus spreads

The concept of contagion is perhaps best thought about in 2020 in the spread of a virus

The structure of social networks directly facilitates the spread of an infectious disease

Viruses spread through networks in a directed path – one individual spreads a virus to another individual, and typically cannot get that virus back from the other

While the contagiousness and severity of the virus are set outside of the network, the density of the network itself can make viruses more or less likely to spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 20 / 23

Behavioral Example: Public Health

The way that social networks shape behavior can tell us something about how a virus spreads

The concept of contagion is perhaps best thought about in 2020 in the spread of a virus

The structure of social networks directly facilitates the spread of an infectious disease

Viruses spread through networks in a directed path – one individual spreads a virus to another individual, and typically cannot get that virus back from the other

While the contagiousness and severity of the virus are set outside of the network, the density of the network itself can make viruses more or less likely to spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 20 / 23

Behavioral Example: Public Health

The way that social networks shape behavior can tell us something about how a virus spreads

The concept of contagion is perhaps best thought about in 2020 in the spread of a virus

The structure of social networks directly facilitates the spread of an infectious disease

Viruses spread through networks in a directed path – one individual spreads a virus to another individual, and typically cannot get that virus back from the other

While the contagiousness and severity of the virus are set outside of the network, the density of the network itself can make viruses more or less likely to spread

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 20 / 23

Behavioral Example: Public Health

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 21 / 23

Behavioral Example: Public Health

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 22 / 23

Next Lectures: Social Networks and [SUBJECT]

We have now covered the fundamentals of network structure, function, and how we affect them, and how they affect us.

Next, we look at how social networks affect:

Health

Social life

Polics

Economics

Protests

Social Media

As we go through this material, start to think about what interests you enough to start thinking through a research proposal. What seems like an area that you would like to do research in?

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 23 / 23

Next Lectures: Social Networks and [SUBJECT]

We have now covered the fundamentals of network structure, function, and how we affect them, and how they affect us.

Next, we look at how social networks affect:

Health

Social life

Polics

Economics

Protests

Social Media

As we go through this material, start to think about what interests you enough to start thinking through a research proposal. What seems like an area that you would like to do research in?

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 23 / 23

Next Lectures: Social Networks and [SUBJECT]

We have now covered the fundamentals of network structure, function, and how we affect them, and how they affect us.

Next, we look at how social networks affect:

Health

Social life

Polics

Economics

Protests

Social Media

As we go through this material, start to think about what interests you enough to start thinking through a research proposal. What seems like an area that you would like to do research in?

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 23 / 23

Next Lectures: Social Networks and [SUBJECT]

We have now covered the fundamentals of network structure, function, and how we affect them, and how they affect us.

Next, we look at how social networks affect:

Health

Social life

Polics

Economics

Protests

Social Media

As we go through this material, start to think about what interests you enough to start thinking through a research proposal. What seems like an area that you would like to do research in?

Gregoire Phillips (UCSD) Social Data Analysis October 19, 2020 23 / 23