>> Andres Monroy-Hernandez: Welcome, everyone. Today we have Ryan Acton. He is
a computational sociologist and assistant professor in the Department of Sociology at
UMass Amherst. And today he is going to be talking about digital traces in online
spaces. So, thanks.
>> Ryan Acton: Great. Thank you. All right, so I have grossly over-prepared for this so I
have way too much material. I'm not going to be able to get through even half of it, so I
apologize for that. To give you a little bit of a background about who I am: I started in
college as a psych and soc double-major -- I actually started as a landscape architecture
major but that didn't last really long -- at Penn State. And then at UC Irvine I got my
Master's in Demographic Analysis and then my Ph.D. in Sociology with Emma under
Carter Butts. And then, in 2010 at UMass Amherst I was hired as an assistant professor
in sociology and also invited to join the newly formed Computational Social Science
Initiative, CSSI. And then also, sort of unrelated, but this year I also joined as member of
the board of directors of a non-profit organization called Help Our Kids based in
Springfield, Massachusetts, and I server as director of web and social media. And that's
sort of a voluntary position I hold there.
Okay, so a little bit about my research interests. Broadly speaking you can sort of break
it down into three categories: first and foremost is social network analysis; -- That's what
I was classically trained in so that's sort of the emphasis of what I pay attention to -- a
little bit more generally though, computational social science; and then thirdly,
development on analysis of data on the R platform. Now more specifically I delight in
problems that allow me to work with processing large-scale data from web-based
sources. And then in terms of the more substantive side of my work, I've tended to revisit
classical social theories with the social behavioral data obtained from computer
mediated contexts. And I'll share with you some examples of that. And then, on top of
that I also have training in demographic methods, GIS, and then survey research and
interviewing methods.
Now in terms of my approach to research, I wanted to share with you a few things that I
find very important and that I tend to pay attention to. So to me online social interaction
is particularly fascinating because of the digital record of interaction that it leaves behind,
these so-called digital traces that is in the title of my talk. A lot of us have come to think
about this as a high tech form of archival research with the Internet being this giant
archive holding all of these digital traces. And of course the ability harvest these data
allows us the possibility for high statistical power. And one of the benefits it has
compared to sort of off-line, old school archival research is a minimization of the human
coding errors which is a big thing to be cognizant of when working with a lot of data. And
then, certainly these online social interaction data allow us to answer and even ask
questions that are not possible with classical forms of data. So I often, because this
realm and these kinds of data, find myself asking is it possible to measure the whole
population and not just the sample? And sometimes quite encouragingly yes is the
answer. And is also possible to automate or semi-automate the collection of those data?
And if yes, that's quite appealing to me because we can do really cool things with it. And
coming out of my dissertation research I learned that if the tool doesn't exist that I need,
go ahead and create it, which I'm sure many of you are familiar with that line of thinking
as well.
Now in terms of the ongoing research projects sort of on my plate, you can break these
down into sort of three different categories. So I started my training with network analysis
in the context of studying disasters. First and foremost I studied networks of
collaboration among organizations in response to Hurricane Katrina. And then, I was
also part of the very beginning of the formation what's called Project Heroic studying
information dissemination on Twitter. I helped build the data collection engine for that
project.
But sort of foremost my work is about measuring social networks online, and so my
dissertation is about methods for web-based data collection. I've developed some
software to aid with this; that came out of the dissertation. And then, I've studied some
sort of specific cases. One of them is studying how groups change in size on last.fm,
and I'll share with you a little bit of that work in addition to the scrapeR software that I
developed. Most recently with a Ph.D. student at UMass studying the entailment
structure of farm attributes among New England farms; I have slides on that in this talk
but I doubt we'll be able to get to them. So I apologize but I'm happy to sort of give you
sneak peeks if needed. And then, another sort of third line that my work is going, and I
mentioned this a little bit earlier, is revisiting classical theories.
So in one case I revisit Balance Theory in the case of social behavior on Epinions.com,
and I'll share with some of that work. And then some work I've done with Emma and our
advisor Carter Butts on extending this notion of brokerage, a very classic notion in social
network analysis to the dynamic case. Okay, so that's sort of me and my background
and now I want to get into the main point of the talk. Okay, so these digital traces are
these things that I find quite fascinating especially trying to capture them, trying to
organize them, store them and then analyze them. And I thought this was very cute. On
a blog that I frequently read, FlowingData, there was this post back in September: How
you know you're in an upscale with these three different grades of gasoline. And the cute
comment beneath says, "Analogue traces or as they're more commonly known, dirt."
And this is sort of the offline analogue to what I am interested in, in the online world. So
there are plenty sort of offline analogue types of traces that can be very messy to study
and analyze. It's quite possible to do that but very messy. I like to bring it into the online
world and study these kinds of markers and traces left behind by people in their
behavior.
And so to do that I developed a software package for the R Platform called scrapeR,
tools for scraping data from web-based documents. And it helps to bring straightforward
web-scraping capabilities to R. You can read the specifics about what it does. But it
particularly is useful when trying to automate the collection of these kinds of data. And
the nice thing is that because R is excellent for data organization and analysis and
scrapeR brings these data directly into R, it's sort of a one-stop solution; you can do all
of this stuff in R. A lot of people like to do Python to do web-scraping stuff. This brings
the capabilities into R. So scrapeR in action, to sort of show you what it does in a very
simple toy case: Here's a website from CISA, the Community Involved in Sustaining
Agriculture. This is a consortion of farms and vendors in Southern New England. A nice
website databases showing you all the different farms and farm stands and farmers'
markets. You type in a zip code and it'll show you what's in your area. So here are the
farm stands in Western Massachusetts. There's Amherst and all here's all the different
farm stands in the area. So wouldn't it be nice to be able to scrape these data out of the
page and get them directly into ours?
So let's say for example you're interested in getting the geographic lat., long. coordinates
of these locations. Well of course sitting behind every website is the source code. And
what I did is I just pulled up the source code for that page and started highlighting the
block of code where that geographic information is sitting. And of course you could pull
this out manually if you wanted to; it would take a while. But using scrapeR, so here I am
in R with a few lines of code and some data cleaning and stuff to get it cleaned up, I'm
able to pull in the page, pull out the lat., long. coordinates and here I have them sitting
over here. And then, I'm free to do with them what I want. So this is just one very specific
example, but you can imagine using this for all kinds of applications where there is
information sitting on some kind of XML or HTML document that you'd like to be able to
pull directly in for analysis. That's one very simple example. So some other potential
applications for scrapeR might include extracting news headlines for a content analysis,
tracking the link structure of a website for a network analysis, perhaps retrieving weather
data for some kind of climate analysis. Where I think the real benefit comes is when
you're able to automate this scraping so that you can repeat it over some interval.
For example, if you wanted to retrieve weather data every 24 hours or you wanted to
capture news headlines every hour, scrapeR can help you do this. This sort of direction
of where scrapeR can be handy is what I found it most useful for in my work. So I've
prepared to present to you three different research applications. I'm probably only going
to get through one and a half here. The first one is one of the chapters out of my
dissertation in which I studied group size dynamics at last.fm. And scrapeR was born
directly out of this project.
So there is very classical questions in sociology particularly in terms of groups, group
identities, group formation, and a lot of times for decades sociologists have been asking
what influences the size of groups because group size influences so many other things
in terms of social behavior. But what is it that predicts the size of groups? And how is it
that groups are changing in their size over time? So these are things that sociologists
have been thinking about for a long time. And in the social network community there's
been a lot of work to show that social networks can drive changes in group size such
that existing members can recruit and attract new members. And this has been
demonstrated in numerous cases. This hopefully is not of surprise to anybody.
So this aspect of trying to examine whether or not we're seeing social network effect in
terms of driving the changes in group size was appealing to me. So in terms of trying to
find an appropriate case study to do this, I wanted to be able to find a context in which
the cost of joining and leaving groups was minimal. It reduces people's barriers to
forming groups. I also wanted to find the case where there were opportunities for
network ties to form and be utilized making people aware of the actions and behaviors of
others. And it would've been nice -- It turns out it was nice. But, I was looking for it to be
nice to be able to have some comparison cases like cross-national comparisons. And so
in my search from data to try to test some of these classical predictors, I came across
last.fm. So perhaps you have heard of it. It's an online music discovery community found
in 2002. At the time of data collection there were over 21 million users in over 200
countries. And among the things that last.fm lets you do is it lets you listen to Internet
radio and gives you music recommendations. It lets you maintain a personal online
profile which contains information about your listening history, your friendship links and
so forth. And it also allows musicians who are holding events to advertise them on
last.fm and let other people indicate whether or not they're going to these events.
And these events were where I ended up focusing all of my attention in this specific
paper. So this is what the front page of last.fm looks like. You can search for any kind of
musician, album or track and it'll give you all kinds of cool recommendations. Tons of
things you can do with this website, but I was focused on this part of the website devoted
to listing the events that are going on in last.fm.
So if you go to the events listing page -- And I didn't do anything fancy. I just went to the
Events page and it showed me, at least as of the other day, which events were
happening most immediately around the country. And you can narrow it down to specific
areas and so forth. So this is the first of at least 500 pages of event listings on this
website. And within a given event page, like if you were to click on The Flaming Lips and
Tame Impala, you see a page that looks like this telling you information about when the
event is, where it is, how to get information about tickets, price of tickets. This
information was not available when I did this a few years ago, at least this part here. This
would've been kind of cool to have. So all kinds of metadata about the event but the part
that was really cool to me was that last.fm users can indicate their attendance
declarations by indicating I'm going or I'm interested. So at the time of me at least
looking at this website there were 44 last.fm-er's indicating that they were going, and you
can also see the listing of who was interested somewhere else.
So this forms the foundation of how I was able to collect or at least what my sampling
frame to collect data for this problem was. What I ended up doing was -- Oh I already
explained all of this to you. What events are. Yadda, yadda, yadda. I already said this. I
saw this as an opportunity to examine all of the event listings on the last.fm page and
look at how people's declared attendance at these events changes over time. This is
how I'm measuring this process of group size formation, group size dynamics over time,
by looking at attendance to these last.fm events.
And going back to the sort of theoretical directions here: there are three potential
processes influencing these dynamics. So I already mentioned to you this network
recruitment process, that the reason why events grow in size over time is doing network
recruitment process. And this has been demonstrated in numerous other avenues. But
another way that events may change in size over time is that some events just might be
differentially attractive than others, so events for very popular musicians might have an
easier time attracting people than lesser known musicians. That has nothing to do with
the network dynamics necessarily.
And another thing that came to mind was differences in group size might be attributed to
how long they've been around. And by "been around" I mean how long has their event
been posted on last.fm, and I'll give some more background into what that means in a
minute. So I'm thinking here are three viable theories that might predict why and how
groups change in size over time or perhaps it's some combination of these processes.
So the networks process: I've already explained this a few times. Here's one group that
has two members and here's the friends of the members. And here's Group B that has
four members and all of the friends of the members of Group B. So this is sort of just to
illustrate the idea that a larger group has potentially a more outreach into a network to
recruit people than a smaller group with fewer network contacts to pull in.
So we should see a proportional rate of growth or at least something faster than a
constant rate of growth as a function of group size. So the prediction for the network
process would be that the rate of growth for a given group will increase as the group size
increases. Yes?
>>: You're saying that's nonlinear then?
>> Ryan Acton: Yes.
>>: So, four people versus two will be more than twice as much in terms of growth?
>> Ryan Acton: Yes. Yes. Very simplistically, yeah. So nonlinear. The other predictor
would be this differential attractiveness process, that some groups are just more
attractive than others. So if this is what's driving fluctuations in group size, we would see
differential attractiveness for groups for a given time interval and that would be the
prediction. And then, the third process I explained is this longevity process. You can sort
of think of it as like a rain bucket metaphor that the longer that a group is there the more
time it has to collect people, and so the differences in group size should be dictated
solely by exposure time, how long it has been around. Three very simple, basic theories
to predict how groups are changing size. I can imagine there are plenty of others out
there. These are just three very simple ones that I chose to look at. Yes?
>>: On last.fm can you see when your friend just indicates that she's going to this
event?
>> Ryan Acton: Yes.
>>: So you get that feed?
>> Ryan Acton: Because you're getting your friends' activity feed, you're seeing when
they're indicating going to an event.
>>: And in your data can you see that? Like who has joined first? Who is doing the...
>> Ryan Acton: Yes, I have the timing of this as well.
>>: Okay.
>> Ryan Acton: Yes, exactly. So I'm tracking -- And I'll get to that in one second. But I
am tracking when people are signing up for these events. So perfect timing. What I did
was I obtained a paired comparison between events going on in the United States and
those going on Germany on last.fm. On the US my data collection started in late October
and ended in early January of 2009. There were 69 days of data collection in which I
was able to observe nearly 3500 events and about 40,000 people declaring going to
those events. And then, in Germany I had a smaller data collection window. And you can
see how many events and attendees I was tracking in that time. It's kind off a little bit,
but this is a large-scale data collection effort because what I ended up doing was
checking the website every 24 hours for how things have changed. And you can see my
basic algorithm here, but what I was doing is that every 24 hours around 2:00 AM Pacific
Time, if I'm discovering an event for the first time I grab metadata for it. I record all the
yes's and maybe declarations of users. Otherwise, if it's an event that I've already been
tracking, I just basically update and see how it has changed since the last time I saw it.
And it's important that I'm doing this every 24 hours so that I'm able to get this
longitudinal time series aspect of the data.
I should also say that I did get permission from last.fm to get these data through web
scraping. And so you can think of an event as having a kind of life cycle. This is sort of
the demographer in me where an event debuts, and I can always capture an event debut
within a 24-hour period because I'm going to be getting to it within 24 hours. The event
will evolve in size over time. People can join or leave. And then, the event reaches its
scheduled date which I'm calling its expiration date. And events acquire their size in one
of two ways, they have debut size and then over time their size is fluctuating through
some kind of dynamic process. Yes?
>>: Who creates the events? Is it automatically created based on some other calendar?
>> Ryan Acton: That is a good question. I am not sure. I imagine people are saying, "I
have an event coming up," and you have to fill out a form. That's a good question. I don't
know the answer to that. So here are all of the events that I was tracking in that window
for the United States. So you can see -- This is my very first day of data collection and
so there were a lot of events already on the website. And so captured them sort of midway through and was able to track them until their end. But there were several events
that I was able to capture for the very first time. And you can see any of these white
dots, since the first day data collection, are events that I was able to detect sort of in
their infancy and then track them as they fluctuated in size over time. And then, my data
collection had to stop in January.
>>: And the red dots are when they happened?
>> Ryan Acton: And the red dots are when the events happened, yes. And it seemed
that I was able to keep tracking them for one or two days after and then, they were
removed from the website. So that's why you see these little tails on these. That's when
the event happens and then it's available for another day or two and then it's gone,
which is why I had to sort of do this every 24 hours because things get pulled from the
website very quickly. That's why I'm like checking this so frequently. Oh, so this was the
United States. You get a sense of the distribution of event sizes. This is Germany. It
looks quite differently but it's also on a very different scale. So I was able to track at least
one event that was over 1,000 people whereas in the US, the largest event was around
250. Interesting bit of an outlier. I never really followed through to see what was going on
with that. But we're looking at the rest of the events are much closer to what's going on
in the United States, much fewer of them because I was tracking Germany in a smaller
time interval.
In terms of the differences between how big an event is at debut and how big events are
at their expiration date, here's what it looks like in the United States. So here's the
distribution of event sizes at debut. And I found this interesting pattern that there were no
events smaller than, what is that, five or six at debut in the United States. I never
followed up with that but I suspect they have a minimum threshold of how many people
can be saying they're going before it can be posted to the website. That's my hunch
because I found the same pattern in Germany. But you can see the distribution of event
sizes when they start and when they day, so that the growth that happened in between
there is what I'm interested in and then trying to explain.
And here it is in Germany: debut size distribution and final size distribution. So it looks
quite similar. Okay so what I'm going to do is I'm, for the sake of the limited time that I
have, going to walk through evaluating the evidence for this networks recruitment
process that I was so eager to try to find in this data. And you'll see where I'm going with
this but I'm going to focus on the first predictor I had: the rate of growth for a given group
will increase as the group's size increases, this network recruitment effect. So what I did
was I tried looking at this numerous was but the easiest way to convey this is that if I
compare an event size yesterday to today, if the point shows up somewhere along the
diagonal, that indicates no change. If it shows up in the lower triangle, that indicates that
the event decreased in size in that time interval. And then if it shows up somewhere in
the upper triangle, that indicates that the event grew in size in that interval.
So this networks recruitment process predication would expect the points to sort of
follow some kind of increasing pattern like this, some kind of geometric growth pattern
over time. That's the generic prediction. Here's what the data looked like. Whoa. Sorry.
Okay, United States on the left. This is with a one-day lag. So what I did was for every
event day I compared its size from the day before to the day after and did the same thing
in Germany. There were 66-some thousand event-days in the US and similar amount in
Germany. And I looked at it with a one-day lag, I looked at it with a seven-day lag, and I
looked at it with a 14-day lag, and it looks virtually identical across the board. And this
was a bit upsetting to me because I was hoping to see much of the point mass in the
upper triangle in both of these countries, and I was not seeing it. This is suggesting that
events are not growing very much and all of the activity is effectively hovering right over
this line of no change. So in any one given day an event may grow by a little bit or an
event may decrease by a little bit, but we're not seeing this increasing rate of growth as
groups get bigger in either country.
>>: When you say events are you talking about the real events that people are
attending?
>> Ryan Acton: Great question. So, yes, ultimately that's what these events being
advertised on last.fm are. Now I'm not realistically measuring those event sizes. I'm not
there at the event counting the attendants in the arena. I'm looking simply at this
community of people on last.fm declaring their interest in going to these events. So, you
know, we have to sort abstract from the idea of being at a Bon Jovi concert to this
community of last.fm people who say they want to go to the Bon Jovi concert is what I'm
effectively measuring here. So great point to bring up. Yes?
>>: The reason I brought it up is when there is a limit to the number of people who can
attend events, right, because they're real bodies attending events. And so, I mean, I
would almost have predicted something like this [inaudible] inhibition. Like the more
people get involved in other events, the more they're not attending this one because they
can only go to one.
>> Ryan Acton: There are so many events they can go to. Yeah. Given, though, that
there's this networked community aspect on last.fm that you can see when your friends
are saying that they're going to these events, I was still hopeful to see some of these
network effects. That if I have 35 last.fm friends and they all saw that I just indicated I
was going to this event, you would think from this network recruitment perspective that
would have helped draw more people in. And for an event that's smaller, you have fewer
chances of doing that. But for a much larger event, you have many more chance of
these network recruitment effects showing up. So at least from the perspective of this
prediction that's what you would expect to see, and we're not seeing it. Did you have a
question, Scott?
>>: That's fine. Go ahead.
>> Ryan Acton: Okay. So I cut out a ton of things because for -- After comparing
different lags between one day, seven days, fourteen days and really finding -- I mean,
I'm going to say this is not a network recruitment effect, so finding no support for this.
What I ended up finding support for were the other two predictions: the longevity
prediction that the longer the group is available on this website, the more opportunities it
has to bring people in, and the differential attractiveness that intrinsically more attractive
groups or popular groups have a better time of being bigger in terms of their attendance
size than these smaller groups. So I did not find support for the network prediction and I
did find support for the other two predictions. So this was sort of interesting even if it's
sort of a non-finding for the network side of things because -- This in and of itself is an
interesting finding because studying group size here allowed me to examine network
effects without actually explicitly examining the social networks. All I was doing was
tracking the counts of the yes's and the maybe's or the yes's and I'm interested's for
each event every day for 60-some days. That's all I had to do for this analysis. Now what
I could have done is actually looked at the who's tied to whom, how large these networks
are and the potential for these recruitment events but I didn't have to, to test this theory.
I didn't have to actually construct the network of who's friends with whom and who's
going to which event to do this which is quite nice; that would've taken quite a lot more
effort to do. So their goal here was evaluate whether this finding holds for other online
group data. This is an interesting case because, again, I'm studying just this microcosm
of this online world associated with these events, does this hold in other kind of eventbased websites? So a natural next step for this that I had begun doing -- I haven't really
done with it in the last couple of years -- is to look at Meetup.com which is much more
explicitly about getting people in the online world to do stuff offline, meeting up in person.
And Meetup has a similar kind of structure where people can indicate their attendance at
these events, data that can also be scraped. I haven't moved forward with that yet. Yes?
>>: Did you [inaudible] rationalize the attractiveness?
>> Ryan Acton: The attractiveness prediction was...
>>: I thought you measured it.
>> Ryan Acton: Oh, yeah. I have that in my backup slides. I can get to that at the end.
Yeah. I was looking at the variance in the growth of the groups in given time intervals but
I have the actual measurement at the end. So I want to move on to my Epinions
example here. So what we just saw was -- Oh, yes? Yes?
>>: So how did you measure the popularity of an event?
>> Ryan Acton: The attractiveness?
>>: Right.
>> Ryan Acton: Right. So that's the same question that Andres just asked. I can show
you; it's at the very end of my presentation in my back up slides. But let me go through
this and I can get back to that. So remind me if I don't do that. Okay, application two of
the three that I have in here is re-examining Balance Theory in Epinions.com. Now
Balance Theory has been a controversial theory in numerous fields for a long time. It all
began with Fritz Heider the psychologist in the 1940's who was inspired by the work of
the philosopher Benedict de Spinoza. Heider developed Balance Theory, and some of
you are likely familiar with Balance Theory as a theory about cognitive and social
dynamics, and more specifically looking at the positive and negative relationships that
exist between two or three entities. Entities being people or things. And in a sort of
nutshell the idea is that like-signed relations in dyads are considered to be balanced;
otherwise, they're imbalanced. So two people that like each other: that's a balanced
relationship. One person who likes the other and the other person not liking the other is
considered an imbalanced relationship. That's at the dyadic level. In triads there are
these sort of four very famous sayings like, "A friend of a friend should be a friend."
People have likely heard of these before. "An enemy of an enemy should be a friend."
These are sort of the predictions of what the signs would look like in balanced
configurations and triads. And in interestingly enough, Heider only ever looks at
transitive triads. He says cycles are not of interest for numerous reasons in terms of
cognitive perception.
It's all about transitive triads: something important to keep in mind. And the fundamental
key for Heider is that balance or imbalance is to be understood from each individual's
perspective in the social structure. And this is where I think a lot of people who work with
balance get it wrong. So the idea is, is that if you've got a triad, you have to understand
how each individual, for example P or O or X, might be perceiving the configuration of
relationships in that triad and that there's not just one master state of balance or
imbalance for the triad, which is what Structural Balance Theory is all about.
Structural Balance Theory was the natural extension about ten years after Heider wrote
his initial piece by Cartwright and Harary in which they framed Heider's ideas as signed
graphs in which they were able to leverage graph theory to evaluate the products of the
signs. And then, they sort of were able to reduce it down to the very simple notion that
positive cycles can said to be balanced; otherwise, they're imbalanced. So already we
are moving into the world of reducing the network into cycles, and Heider is quite
adamantly opposed to looking at cycles. He thinks cycles are of no interested with
respect to balance.
>>: Can you define cycles?
>> Ryan Acton: Sure. A cycle is: I like Andres, Andres likes you, you like me. It's sort of
the cycle -- It's moving in this cyclic direction. As opposed to transitivity: I like Andres,
Andres like Emma, I like Emma. So it's not going in a cycle; it's going that way and this
way. I'm sorry. I usually have slides on this up here and I left them out for this talk. Yeah,
these are terms that we use a lot in networks. So for Heider this cycle-notion of the
flowing of the direction of liking is not interesting. It's this transitivity. If Emma is the
common target of our liking, Andres likes her, I like Andres so I'll like Emma is the logic
that Heider was talking about. So in the world of Structural Balance Theory balance is a
property of the social structure and it's not of the individual perceptions, very different
take compared to what Heider was talking about.
This is mathematically and computationally appealing because if we can treat balance
as just a system of signed graphs then we have all kinds of methods for relatively easily
computing these balance properties on the network. But this gets us away from the
intuition that Heider was trying to get us to talk about, and in fact because it's
mathematically and computationally appealing, at least this approach to balance, a
majority of sociological and computational social science has started with this Structural
Balance Theory as the direction to go and ignored effectively what Heider was saying.
So while Structure Balance Theory was inspired by what Heider came up with -- these
ideas that people have differential perceptions of what's going on in the network and that
determines how they feel about whether their social configuration is balanced or
imbalanced -- Structure Balance Theory takes individuals' perceptions out of the picture
completely.
>>: To me it's like the fundamental difference between the social psychologist and
sociologist, recognizing the [inaudible] nature...
>>: Yeah. Reducing it strictly to structure, and people have no input any more. That's
exactly what happens here. Yep. So as a sociologist and as a social network person, I
should really be thrilled with this. I should be on board with this 100 percent. And I'm not
because I don't think this is how it works. So the structural balanced theorists are looking
at the network as a bird's eye view. It's not from an individual point of view; it's from the
bird's eye view. I'm not going to go through all of this, but for Heiderian Balance Theory
empirically there is mixed support. There's lots of different stuff that's been done, mixed
support. For Structural Balance Theory: mixed support. Depends on the context. There's
been a lot of theoretical work done. There's been a lot of simulation-based work done.
There are people who outright critique the integrity of Balance Theory thinking it's just
trash. And then, there are other people who point to social mechanisms other than
balance that can predict the same outcomes. So there is a very colorful background of
work in this line of work, but very few people have systematically examined the effects of
heterogenous perception in the network.
So taking the Heiderian approach of factoring in individual's perceptions seems to have
all been forgotten in the last several decades. And what really got me fired up about this
was the paper that came out in 2010, Jure Leskovec and colleagues, in which they
studied balance on Epinions.com from the Structural Balance Perspective in which they
leave individual perceptions out. And I feel like they missed the most important piece of
what I'm about to show. So basically they test Balance Theory on the Epinions data.
They find support for it. Good job. Let's move on. And I argue that, okay, but you haven't
really studied the most important part here. So this motivated me to bring the Heiderian
perspective to the study of balance on online social networks.
As many of you know many of online social networks allow people to tag others as
friends. It's quite less common for you to be able to tag people as foes or enemies. Very
few social networks allow you to do this. Slashdot is a very longstanding network that
has allowed you to do this for a long time. Epinions happens to be another one. And
there some others out there, but for Facebook, for example, there is no outright way to
say, "This person, I just don't like them." You can block them which maybe is sort of
what that means. But there's no easy equivalent to the friend relation. So this led me to
Epinions.com data for this reason that you have the ability to sort of friend and foe
people on Epinions.com.
Epinions.com is a product review website started in the late nineties and is currently
owned by Ebay. It's free to join. You can browse other people's reviews of products in
your effort to search for a new product. It helps people in their product purchasing
decisions. And there are three main types of relations between Epinions users that I'm
using in this analysis. So this is actually a departure from the theme of the talk because I
actually didn't use my scrapeR package to get these data. These are the same data that
Jure Leskovec and colleagues used in the paper I just pointed out. I obtained them from
Trustlet.org, a great repository for trust-based data.
And the data span the inception of Epinions.com in the late nineties through the 12th of
August 2003. This contains all of the relational activity among these people in that time
span. And it's freely available to download. Anyone can play with those data. What is so
cool about these data and why I'm able to bring a Heiderian perspective to balance with
these data is because the negative relation in Epinions, the sort of foe relation, is
censored from their recipients. So in other words, they call it trust and distrust on
Epinions.com. You can either a fellow Epinions.com user or distrust them. When I
distrust you, you don't know about it. Only I know about who I trust and distrust. So to be
the recipient of distrust on Epinions.com means that you are censored from knowing that
information. So from your perspective all you know from the website is that there is no
incoming tie from that person. There is obviously no trust tie coming from them because
they don't trust you, but there's no indication that they distrust you. So from your point of
view, you do not know that they distrust you. From the bird's eye point of view we have
perfect knowledge of who trusts and distrusts whom, but the recipients of distrust do not
know this. So this sets up a nice case to test differential percepts of trust in the network
on Epinions.com.
The three primary types of social relations that I'm able to look at are article authorship,
trust-distrust relation and article evaluation. So I have these sort of cute little cartoons to
explain this. So on Epinions.com you can write product review articles, so that's an
example of the authorship relation. Person O wrote the product review Article X. That's
one kind of relation on the website, and that's how many of them there are in these data.
The trust and distrust relationship: you can either trust or you can distrust or you can
choose not to have either with a person. So here are all the ways that people can be
dyadically connected through trust and distrust, going from two people who have nothing
to say about each other to two people who distrust each other; keeping in mind that this
person knows that they distrust this person but this person is not aware of it, and this
person knows that they distrust this person and this person is unaware of it. And
everything in between. Yes?
>>: Question about your data. So you said that users don't know about this distrust
relationship, but you know.
>> Ryan Acton: Yes.
>>: But is it anonymous? I mean if they had access to distrust data then they will have
found out who...
>> Ryan Acton: The data were anonymous in the sense that user names were stripped
out and given new ID numbers. So I don't think they could reconstruct it. So I don't know
who these people are specifically; I just know them by some generic ID number.
>>: Okay.
>> Ryan Acton: Yep, exactly. So that's the trust-distrust relation. And then, finally is the
evaluation relation. So I already pointed out that people can be the authors of product
review articles and third-parties can evaluate someone else's product review article in
terms of liking it or disliking it effectively. So with this web of these three kinds of
relations, I'm able to examine different kinds of cool ways that balance forms or
imbalance forms in this network.
And what I'm doing here specifically is modeling the formation of new trust and distrust
ties that either closed dyads, transitive triads or three cycles. Remember Heider says
cycles are not of interest, but Structure Balance theorists say they are so I threw them in
here because some people at least think they're important. And what I'm doing is
evaluating the effect that each new trust or distrust tie has on the states of balance or
imbalance in these configurations. So when I form a new tie to somebody in the network,
be it trust or distrust, does that then create a balanced situation for me? An imbalanced
situation for me? Or did that do nothing to change the situation? So by my adding new
ties to the network, is that upping or decreasing my balance and imbalance?
>>: From ego.
>> Ryan Acton: From ego's perspective, correct. Yeah, because Heider that's all that
matters. It's not a structural thing any more; it's just want the individual people are
perceiving. Okay, so what I used here was a multinomial choice modeling framework
which has effectively a logistic regressing interpretation. But what I'm looking at is what
is the probability that the Choice C of signed tie -- Meaning I have the choice of either
trusting or distrusting or doing nothing with you. What is the probability that the choice of
the signed tie is the one that's actually observed? So you're sort of faced with a new
situation. As I form a tie with you, I could've done numerous things. What's the thing that
I ultimately picked and what effect is that having then on the states of balance and
imbalance in my local triadic and dyadic configurations? So basically, am I behaving in a
way predicted by Balance Theory? Am I creating the kind of tie that's needed to
generally increase my balance levels or am I creating ties that go completely against
what Balance Theory would predict?
The full model results are here, but what I'm basically going to do is zoom in on the full
model to share with you the interesting outcomes from here. So let me just quickly
explain this table and then I'll wrap up very shortly here. So this is the term for me having
created a balanced dyad. By virtue of a tie that I just created, that created a balanced
dyad for me. According the Balance Theory, the expected coefficient in the model should
be positive. We should see an increased likelihood that people will create balanced
dyads and will shy away from creating imbalanced dyads. What do I find? I find support
for the creation of balance dyads and the term for the avoidance of imbalance dyads is
non-significant. Meaning, some people choose to shy away from forming imbalanced
dyads; some people choose to do nothing. And this is sort of saying that both strategies
are likely happening here. When it comes to creating that closing tie in transitive triads,
there are different ways this can happen. So, I can add a positive tie which will create a
balanced triad. I can add a negative tie to create a balanced triad. I can add a positive tie
to create an imbalanced triad. And I can add a negative tie to create an imbalanced
triad. But the thing to look at is am I seeing effects for these and am I not seeing effects
for these. Once again we find support for the creation of balanced triads. Here we see
no significant tendency for people to stay away from this scenario, and here we do find
support for using a negative tie to create an imbalanced triad.
And finally this one is a little bit puzzling. Remember Heider says cycles don't matter.
Structural Balance Theory says cycles are where it's at. So will my addition of a tie
create a balanced cycle or will my addition of a tie create an imbalanced cycle? This
should be, according to Structural Balance Theory, this should be preferred and this
should be avoided. And I find the opposite. Yes.
>>: Are you going to get into why you think those results there?
>> Ryan Acton: Yeah.
>>: Okay.
>> Ryan Acton: Yes. Well, I can say it now but I think I have it on the next slide here.
>>: You're saying you're avoiding imbalance? So you actually remove a tie? Type 1 and
Type 2 imbalance, I'm just...
>> Ryan Acton: So I'm looking at two different kinds of things that can be done. Does
the addition of...
>>: Adding and removing a tie, is that right?
>> Ryan Acton: You're just adding a tie. You're not taking anything away. I'm jus
watching the creation of new ties in the network and seeing, are you adding a positive tie
or a negative tie and is that creating a balanced or an imbalanced triad? Because you
can imagine...
>>: Tie meaning I have tie where I don't like them? Or [inaudible]?
>> Ryan Acton: So positive tie means I'm establishing a trust tie with you and does that
make the triad I'm involved in, does that now become a balanced triad? Or it's also
possible for me to add a positive tie to create an imbalanced triad because there was
already a negative tie somewhere in the triad versus adding a negative tie to create a
balanced triad or adding a negative tie to create an imbalanced triad.
>>: I see.
>> Ryan Acton: So it all hinges on what I'm about to do. What is the tie that I'm about to
create and what is that doing for the states of balance? This one is interesting because
Heider says this doesn't matter. Structural Balance theorists say this is what should
happen. I find the opposite. That leads me to suspect that cycles are not accurately
capturing what's going on with balance because cycles -- Do I have it on this slide? Let
me see. Let me go through what I have already on the slide. I generally cautiously lend
support for Heiderian Balance Theory over the predictions given by Structural Balance
theory. So we do see significant tendency to create balanced dyads and transitive triads.
To establish balance is cheap and it's desirable. To establish imbalanced, though, is
expensive. It can induce cognitive dissonance. There could be potential social backlash
for engaging in the creation of imbalanced triads. And because of some of those nonsignificant findings, it would appear that doing nothing might be a passive aggressive
way of avoiding the creating of imbalance because it's a lot less uncomfortable for you to
just sort of step away from it than to actually actively go ahead and create that
imbalanced tie.
So I don't have on here what I think is going on with cycles. So the gist is I'm arguing
that this lends support for Heider's argument. It sort of doesn't really do anything
because Heider doesn't even think this is important. But this is the opposite of what
Structural Balance theorists say is happening. And Heider says in cycles it doesn't
matter. Structural Balance theorists say cycles are where you capture it. Cycles are
problematic because cycles -- Let's see if I can get this right. I have no control over the
balance in the cycle because all I'm doing is creating the -- Let's see here. Andres like
Emma. Emma likes me. And now it's up to me to create that closing tie in the cycle to
determine balance. But from my perspective if Andres doesn't like me, I don't know
about that. Remember from the censoring in the ties? So I don't even know that that
cycle exists. I don't even know that balance or imbalance is even possible because I
can't even see the incoming negative tie to know what the right move is to make to close
that cycle. So from Heider's argument this is pointless because it's about how I perceive
the network. Whereas from a bird's eye perspective this is how you could capture it but
you're taking the individuals out of the picture.
So this is sort of lending support to suggest that there is really kind of fuzzy business
going on here. We don't really even believe that people are behaving from a cyclic
perspective.
>>: I think [inaudible] on the cycle.
>> Ryan Acton: Yeah, they found when they don't factor in individual's perceptions, they
find support for these -- they find the predicted result. But when you factor in how people
are actually likely to see what's going on, I see the opposite. So the third example I don't
have time to get to -- Very cool application and I'm happy to share it with you all later, but
at this point what I'll do is I'll open it up to questions if there is any time.
>>: So the study that you had mentioned, was it on Epinions or on a different...
>> Ryan Acton: Same exact data set.
>>: Same exact data set. This one. Yep. But it's not surprising because they are coming
at it from a Structural Balance perspective. They're not thinking about individual
cognition to the network. And to be fair, neither am I. I am not actually asking these
people to share with me how they are perceiving the network. So to be fair, I'm violating
that as well. What am I doing, though, is modeling this with realistic assumptions about
how people might be perceiving these ties in a way that they're not even attempting in
this paper. So I agree with their findings to the extent that they're using a Structural
Balance theoretic approach. My argument is, is that's not the approach to take. You
cannot accurately capture balance when you're not factoring in differential perceptions
from individuals in the network. So that's sort of the beef I have with this line of work.
>>: You could do two models together and try to like explain the variants that it explains.
Do you have a sense for -- Against each other...?
>> Ryan Acton: Oh, against each other. I haven't done that.
>>: [Inaudible] how is adding that perception...
>> Ryan Acton: Right. No, I have not done that yet but that is a great suggestion. Yeah.
>>: So the non-significant finding at the top of the slide there slightly goes against the
Heiderian...
>> Ryan Acton: Yes, it does. Slightly.
>>: So maybe -- So I guess I'm just curious why you think it is? But one possibility is, at
least in this case, there might be value actually in sort of having a connection to
someone with an opposite opinion of you. Like the opposite opinion might actually be
informative in some way even if just to be interesting or verify your side of the argument
or something along those lines....
>> Ryan Acton: Right. So it's sort of safe to maintain that...
>>: Right.
>> Ryan Acton: ...connection.
>>: Well, there just might be a value there that overrides this notion of balance.
>> Ryan Acton: I totally agree with you. Totally agree with you. From the way that I'm
modeling in here though, keeping in mind, I cannot know that you disagree with me -- or
I cannot know that you distrust me. So I don't even have that knowledge. I might know
about it through other means on this website. You may have flamed me somewhere else
on the website or said something awful about me. But in terms of my knowing directly
whether or not you distrust me, I don't even know that which is kind of fascinating. But
we have the luxury of being able to take the bird's eye view approach and see what
actually happened, who actually did like and dislike whom on the network to see our
people fitting in. Yeah?
>>: I have one other question which was kind of implicit, I guess, or I've always thought
it implicit in Balance Theory is that there's an equal likelihood of positive and negative
connections.
>> Ryan Acton: The propensity to form one or the other.
>>: I mean you can have balance with positive and negative connections with equal
likelihood for being positive and negative connections. Is that true with...
>> Ryan Acton: No.
>>: ...networking? I'm assuming there is way more positives than negatives.
>> Ryan Acton: Yes. So I have determined the model for the number of positive ties.
The general propensity to form positive versus negative ties and it's overwhelmingly a
strong effect. People much prefer to be nice effectively than to be mean. And there has
been support for this finding in numerous contexts. From the pure theoretical point of
view, they should be equally likely but in terms of empirical observations people have
found this finding in numerous situations.
>>: Right. So actually then it almost says even more if people create balance through a
negative tie.
>> Ryan Acton: Yes. Because it's a much less likely behavior to be engaging in.
>>: That's a stronger signal.
>> Ryan Acton: Yeah.
>>: Yeah.
>>: So you said what's the advantage of marking somebody as a...
>> Ryan Acton: When you mark somebody as distrusted, they -- Oh, I had the slide for
this -- you are less likely to see their product review article in your feed among a few
other things. They become less salient to you. Yes?
>>: And for positive do you see more often the reviews?
>> Ryan Acton: Yeah, it's sort of like following on Twitter. If I follow you now, you're part
of my feed. And if I ultimately decide to change that to a distrust, it pushes you out of my
salient scope. Yeah. Yeah.
>>: So that might explain the non-significance as just the sheer likelihood of that
happening is not that high.
>> Ryan Acton: Yeah, I think so. Yeah, right because this is a much more favorable and
common behavior in the first place and that's capturing most of what's going on. Yeah.
Yeah.
>>: And what about the non-significance for the Type 1?
>> Ryan Acton: Yeah, I mean this is -- Yeah, I'm not exactly sure what to think of this.
By my adding a positive tie which we know is a more likely outcome than to add a
negative tie, I'm creating an imbalance triad. And this is suggesting that there is really no
effect of -- compared to doing nothing, there's really no significant tendency to do one
thing or the other. People are just as likely to do nothing than to engage in this behavior.
So this might be an avoidance strategy. This I might be, "I know that by liking you I can
create an uncomfortable situation, so I'm just not even going to go and create that tie," is
what I think might be happening. Whereas knowingly adding a negative tie to create an
imbalanced situation, we are seeing the predicted effect here. People are behaving
according to the theory. So this I think is sort of a cheap, easy avoidance strategy.
>>: But as far as the negative tie, other people can't see that. So it's like, "I'm going to
do this but...
>> Ryan Acton: But it's a big secret.
>>: ...personally [inaudible]..."
>> Ryan Acton: Nobody else knows. From my point of view, right, everything works just
fine.
>>: But the positive one everyone else can see and so maybe people don't want to go
there.
>> Ryan Acton: Exactly.
>>: How do you know people don't know -- How do you know that people are aware that
the negative connection is not displayed? Like on Twitter, for example, I can block you
and...
>> Ryan Acton: Yeah.
>>: ...until I unblock, they don't know that the blocking actually is public.
>> Ryan Acton: So Epinions builds this into their system. They want to prevent -- Their
logic is they want to prevent hurt feelings, so they do not let recipients of dislike...
>>: But they show that -- Like when you're about to block somebody does is say, "By
the way what you're about to do is not going to be public?"
>> Ryan Acton: I don't -- That's a good question. I don't know that.
>>: Is there actually -- It is such a strong social norm to not provide negative feedback.
>> Ryan Acton: Yes.
>>: People will avoid doing it even if they're kind of confident that a person find out; they
still won't.
>> Ryan Acton: Yeah. Whereas Epinions, this is part of their system. I imagine when
you sign up you are informed somewhere in a long policy that your negative actions are
not seen by others. Or perhaps when you go to block someone, it lets you know. I'm not
an active Epinions user and I've never tried disliking someone to actually know. That's a
great question, though, to know what the user is seeing before they do that. Yeah?
>>: One thought I had, just it's kind of a curiosity, I wonder if there are certain people
who are much more likely to be in imbalanced situations? I don't know. The controversial
people, inflamers...
>> Ryan Acton: Yeah. And who don't care about...
>>: Yeah.
>> Ryan Acton: Yeah, I imagine.
>>: The incendiary type. Or maybe it's just person type or something.
>> Ryan Acton: Yeah, and admittedly I'm not capturing that here.
>>: You don't know [inaudible] the users, right?
>> Ryan Acton: No, this dataset it strictly the tie formation.
>>: [Inaudible]
>> Ryan Acton: Nothing else about them, yeah. And from that this is what I'm able to
gather.
>>: But you can see -- You could actually cluster people or something, right, and
observer how balanced they are compared to...
>> Ryan Acton: Sure. You could.
>>: I mean, right?
>>: Yeah.
>>: There could be some people that are like totally balanced all the time and then other
people, like we were saying, that are like really unbalanced.
>> Ryan Acton: Right.
>>: Or maybe it's just if you have very few ties, it's easy for you to maintain the model to
be balanced. If you have a ton of stuff, maybe you're more likely to be imbalanced....
>> Ryan Acton: Right. And you don't even care at that point because you can't even
keep track.
>>: Yeah, but that's something maybe you could look at.
>> Ryan Acton: Totally.
>>: The degree versus...
>> Ryan Acton: Does the size...
>>: ...the proportion...
>> Ryan Acton: ...of one's local network influence it? Good point.
>>: That'd be cool.
>> Ryan Acton: All right.
>>: Do you feel like with either this study or the last that are there any like kind of
implications for product changes? Like it sounds like [inaudible]...
>> Ryan Acton: Yeah.
>>: ...the positive stuff. And the negative -- So like can you draw applications like, "Oh,
that might not be a useful feature?"
>> Ryan Acton: Yeah.
>>: Or, you know, things...
>> Ryan Acton: No, it's a good question. I mean especially with the last.fm thing,
perhaps when it comes to the possibility of recruiting your friends to these events, if they
would like to see that happen -- which I imagine they would, they would like to draw
more attention to events -- then perhaps this suggests that they might do a better job of
making your friends more aware of what you're doing. That might be one potential
intervention. For Epinons.com, I mean, at the end of the day this is just trust and distrust.
I don't know how useful of a relation on Epinions this is other than for you to be able to
block people out of your scope. I would imagine a more realistic environment to try this
out.
>>: Probably the [inaudible] important for design [inaudible] should be aware and
thinking about people's perception, in particular behavior.
>> Ryan Acton: Sure.
>>: Versus this bird's eye view [inaudible].
>> Ryan Acton: Right.
>>: Did you do any suggestion on the [inaudible]? Oh, if you had ties to these people, if
you trusted these people, this would all be balanced. So may I should suggest
[inaudible] good people...
>> Ryan Acton: And in the event that that somehow increases revenue for Epinions,
right, by keeping people in balanced states that might draw revenue better. Whereas
people being disgruntled or engaging in disgruntled sort of behavior, yeah. Yeah, and as
a non-active user of Epinions, I don't really even care what these people do at the end of
the day. But it's fascinating to see that when all I know is just the tie structure and
nothing else about them, by and large they're adhering to most of what Heiderian
balance has to say. So to the extent that this could be -- this continues -- I mean these
data are in the early days of Epinions. Maybe with this site's changes over the years,
things have changed. If we were to repeat this and we continued to find this here in
another context then perhaps we can use this information to figure out how to optimally
pair up people to increase...
>>: Another as far as like implications, one thing that would be really interesting to look
at is if you were looking at these balanced cycles versus the Heiderian balance systems,
they differentially predict engagement within the system.
>> Ryan Acton: Sure.
>>: And how long do they stay or how much do they tend to grow [inaudible]...
>> Ryan Acton: Or people who find themselves overwhelmingly imbalanced might just
retreat from the system. Yeah, good point.
>>: So then an implication would be to really help the users find people that they think
would be balanced -- You know, like you could easily draw like...
>> Ryan Acton: Suggestions.
>>: ...[inaudible].
>> Ryan Acton: Yeah, these are all good points. Thank you.
>>: Can you go back to the question of how you measure attractiveness?
>> Ryan Acton: Yes. Go ahead and keyboard. Oh here we go, okay. Oop. So I am
testing for homogeneity of the variances of group size for specific observed time
intervals for these different groups. So I'm effectively looking at how varied are group
sizes as a proxy for this attractiveness measure or how popular these events likely are to
be. And again the prediction as for any given time interval, groups should different
significantly in their rates of growth. I mean, it's almost a no-brainer prediction but I find
support for it; whereas, I don't find support for the network recruitment prediction which
was shocking to me. I would've expected to find this anyway, and I did not find the
network recruitment prediction to hold.
>>: But that doesn't really tell you much about the -- like if you have Jon Bon Jovi
versus some randomly old band.
>> Ryan Acton: Yeah.
>>: And you don't have any other information about this, you cannot...
>> Ryan Acton: Right. I don't have any separate metrics of attractiveness other than
this.
>>: Just through the [inaudible].
>> Ryan Acton: Correct.
>>: I was actually surprised by the size of the events and I was wondering if that might
have something to do with it? Because the average size was, what, 200 or something?
>> Ryan Acton: Yeah, let me...
>>: But these are concerts or --?
>> Ryan Acton: Concerts, festivals, various kinds of music.
>>: So it's really people expressing an interest in an event to show others that they're
going....
>> Ryan Acton: Right. This is what I'm doing.
>>: [Inaudible] RSVP'ing to the event or something like that.
>> Ryan Acton: Right. Yes.
>>: Well, there is the I'm going versus I'm interested.
>> Ryan Acton: Yeah, but still I don't have any then followup measure of if you actually
went.
>>: You're not committing -- Like it's not even the event organizers creating these
events. It's just not like...
>> Ryan Acton: Yeah, it's more like on Facebook event case, it's more of an indication
that you're actually going because I know people use it for RSVP.
>>: Yeah, so I wonder if there's even -- Right, like how much are people aware that this
person is interested in this event?
>> Ryan Acton: And how much are people paying attention to it. Yeah, right.
>>: And also if there's a cap on the event size. I know these events do tend to be in
small venues where only 100 would go anyways.
>> Ryan Acton: But keeping in mind, though, that I'm not -- Last.fm is not capping it
based on the venue size. So again this is an online community forming around this
offline event.
>>: But I have actually seen -- [Inaudible] -- a study looking at as event size increases
the explicit RSVP's go down. [Inaudible]...
>>: Yeah, that's actually a question I was going to ask. Can you go to the actual
[inaudible]...
>>: [Inaudible] of responsibility...
>> Ryan Acton: For the network or the...
>>: Yeah, the next slide. That was what I was going to ask. Yeah, right there.
>> Ryan Acton: Yeah.
>>: So if you zoomed on event size being small, it slightly looks like you do get above
the line.
>> Ryan Acton: Right.
>>: But then it starts to go...
>> Ryan Acton: And then it narrows down.
>>: It's almost. That's the question I was going to ask. It sort of looks like...
>> Ryan Acton: The effect might hold in smaller groups.
>>: Well, initially and then it just kind of [inaudible]...
>> Ryan Acton: As it gets bigger people feel less compelled to RSVP. So that very well
could be happening. Yeah, I mean you sort of see it narrowing. What's that?
>>: That's pretty nuance at that point. I mean it's -- Yeah.
>> Ryan Acton: But it's clearly nothing like the pure network recruitment prediction
would predict. It's clearly not that. Maybe on some very small scales at certain levels
but...
>>: And the other thing that might play a role in this is that friends on last.fm are not all
located in the same geographic base?
>> Ryan Acton: Sure.
>>: I assume a lot of people friend each other because they have similar...
>> Ryan Acton: Similar music taste.
>>: ...but they might live [inaudible]...
>> Ryan Acton: Very good point.
>>: ...so they cannot attend the same event.
>>: Very important actually.
>> Ryan Acton: Yeah.
>>: I would strongly recommend doing the same analysis on either Meetup or
Facebook.
>> Ryan Acton: Right. Where people are likely to be co-located which is the next natural
step for Meetup.com.
>>: And the notifications for friends, find out that you're at this event [inaudible].
>> Ryan Acton: Yeah. Yeah, the whole reason -- Because you -- Yeah, I totally agree.
Yeah.
>> Andres Monroy-Hernandez: Let's thank Ryan.
>> Ryan Acton: Thank you.
[Applause and voices]