Notes

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So, we've just completed study number one.
Study number one was looking at
the spreading of lead users through
neighborhoods.
Now what we're going to do in study two,
is we're going to look not at
neighborhoods directly.
We're going to look at actual individuals
influencing
each other in a social networking site.
This study was conducted by some
colleagues at
UCLA and also at the University of
Maryland.
So let me go through the problems or the
challenges first, that these authors faced
in trying to do the study.
First challenge they face is that if
you're a friend of someone on a social
network, that could be a very strong tie,
or it could be a very weak tie.
That friend could be your best friend that
you grew up
with and you've known since five years of
age, or it
could just be somebody that you met at a
function and
you exchanged business cards and became
connected as friends on Facebook.
So the first challenge that they faced in
looking at these social networking sites
is what a
friend actually means.
For some people it could mean a very, very
strong thing, for others it could be very,
very weak.
So they didn't want to necessarily look at
connections among friends per se.
The second thing was kind of challenging
is these databases
are huge and the number of connections is
very, very large.
So they had to think of some simple
statistical way of trying to get at that.
And the goal of the study was to try and
understand who's influential for whom?
So, if Chris and I are connected on
a social networking site, am I influencing
him?
Or is he influencing me?
That was the goal of the study.
So, for those of you who enjoy a little
bit of history, I did some digging around.
And it turns out that the very first
social networking
site, at least in the United States, was
one called classmates.com.
Since then, at least in the US, we've seen
many come and go.
There's been Friendster, there's been
MySpace,
and of course now there's Facebook.
And who knows,
Facebook seems here to stay with over
a billion people currently part of that
community.
So, the researches wanted to understand
who
in the network is influential for whom?
So, what they decided to do since a
measure
of just pure friendship is not that
diagnostic A friend
could be someone that I barely know, or a
friend could be someone that I've known
for 20 years.
So the way they measured influence was
quite clever.
What they did was, tried to figure out if
my
activity in the social network was
influenced by somebody else, meaning after
they did something, I also started to
follow their activity as well.
So to go back to the example of Chris and
I, let's say being connected on Facebook.
He's going to be influential for me, if
after he starts posting content and
photos and videos, I start going to his
site and start looking at it.
I'm not influential for him if I'm doing
those activities,
but that's not affecting his activities at
all.
In simple terms, that's what the authors
were doing here with this study.
So, what did they find?
Well, they wanted to try and figure out
who was
going to be important and who was not
going to be important.
And on average, how much influence goes on
on a social networking site.
Now, if you think back to some of the
terms that Pete mentioned in his part of
the course.
Probably the word that he mentioned the
most
knowing Pete, I haven't done the exact
count,
is the word, Heterogeneity.
Heterogeneity is one of the great buzz
words in the marketing course here of
course.
Just means people are different, and we
have to understand the extent of those
differences.
So, those huge variation, they found in
the level of influence that was going on.
Some people were highly influential.
Some people were not influential at all.
Others were highly susceptible to
influence.
Others were not influenced by people at
all.
So what do those numbers kind of look
like?
Well, here's the bottom line from the
study.
The authors found that on average, you are
influenced about, by about 20% or 1
5th of your friends on Facebook, or
LinkedIn,
or whatever other social network you're
active in.
About 1 5th of them are influencing you,
and the other 80%
or so are not really having much sway over
your behavior at all.
Now, if we turn the problem around, this
statistic to me is also very, very
interesting.
They found that about 1 3rd of the people
in the social networking site were not
influenced by anyone.
These are kind of the maverick people who
just do their own thing.
And they don't worry too much about who's
posting what, and
other things that are going on on a social
networking site.
So, you're influenced by 20% of your
friends.
About 30% of you out there are not
influenced by anybody.
Now let's sort of dig under the hood
a little bit and try to understand the
extent
of variation in influence, and what causes
influence in a social networking site.
So, let me now explain the blue histogram
that you see in front of you.
This is just a histogram taken from the
original article.
What it's showing is, the amount of
influence that friend f has on user u.
And what you can see towards the left hand
side of the chart, is
there are many people whose influence
factor, if you like, is very, very small.
Close to zero.
And on the right hand side, it's a
little bit like a longtail diagram, again,
and the
right-hand side, at the extreme level,
there are
some people, a smaller number, who are
hugely influential.
And, on average, about 20% of the social
network,
people in the social networking space, are
influencing other people.
So, what you can see on the screen now is
another chart from the paper.
And I'm just going to explain the key
results here.
I think these results actually
are very interesting.
actually, fascinating results.
And things I think that we could not only
use,
but maybe also sort relate a little bit to
our intuition.
So, the first thing the authors found was
someone's who's been on a social
networking site
for a longer period of time, on average
is more influential than somebody who's
just joined.
I think that makes sense.
That's a nice statistically significant
effect.
The second effect, which I think speaks to
cultural background as well as ethnicity.
Is that people
who are from the same ethnic or cultural
background, on
average, have more influence over each
other than just random people.
This is again partly due, I think, to
homophily.
So, I'm more likely to be influenced by
somebody who's from the Australia, New
Zealand kind of part of the world, than
somebody who's just coming at random.
The next thing that they looked at was
gender influence.
Now this one I find particularly
fascinating.
So of course, there are two genders.
And four possibilities
for influence.
Man could influence woman, man could
influence man,
woman could influence woman, or woman
could influence man.
Out of those four possible combinations,
there
was only one statistically significant
path of influence.
The guys and the girls out there can
probably relate to this.
Girls were influential over guys, but not
the converse.
And again, think about what the definition
of influence is in this case.
The definition is, when somebody is
engaging in
activity on the social networking site,
posting, commenting, and so
on, other people are checking that out and
following along.
So when females do that, males follow
along, but not the converse.
So there's actually a lot of interesting
research
that's being done in the area of gender
segmentation on the internet, and I think
this
is just another finding that plays right
into that.
The final result that they found is to do
not with who you are as
a person, or how long you've been on the
site, but what it is that
you talk about, and what you say when you
get there, and how you present yourself.
So we've already discussed reputation and
review.
This is a little bit of your personal repu
reputation.
It turns out, if you're on a social
networking site, and you're
indicating that you're looking to date
other people, that significantly reduces
your influence.
So maybe think about that before you you
start posting too much.
Okay.
What are the implications of this?
If we want to advertise on social
networks, or
we want to run social networks, and so on.
There's really three things there that I
put on
the slide, but let me just go through
them.
First of all, simple counts of who is a
friend with whom, are not really
sufficient to understand influence.
Because sometimes a friend can mean a
really really
close friend, other times it's a person
you've just met.
There's really too much variation.
So we need different ways
to measure who's influential on a social
networking site.
Secondly, the authors found when they did
some simulations, that if you take the
very best people out of the social
networking site, that dramatically reduces
the value.
So just like in the real world, there are
some special people who have
disproportionate influence over others.
That's very, very important to keep in
mind.
And then the final point that's related to
the one that I just made.
Is if you want to advertise on a social
networking site, or you want to use a
social networking site to promote the
products and services
that you may be wanting to offer to
people.
Most of the payoff you get, is from
identifying the very best and most
influential people.
Since many, many people aren't influential
at all, there's great returns
to figuring out those who are the best in
this environment.
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