[MUSIC] 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 number 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 this study. First challenge they face, is if you're a friend of someone on a social network, there 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 that was kind of challenging, is these data bases 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 U.S., 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 researchers 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 measure the influence was quite clever. What they did was, try 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 them. I'm not influential for him if I'm doing those activities, but that's not effecting his activity 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 in 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 our great buzzwords in the marketing course here of course. It just means people are different and we have to understand the extent of those differences. So there was huge variation, they found on 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 are those numbers, kind of look like? Well, here's the bottom line from the study. The authors found that on average you are influence by about 20%, or one fifth of your friends on Facebook or Linkdin, or whatever other social network you're active in. About one fifth 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 one third 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 in the 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 on 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 in the right hand side, it's a little bit like a long tail 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 we can not only use, but maybe also relate a little bit to our intuition. So the first thing the authors found was, someone who's been in 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 homophyly. So I'm more likely to be influenced by somebody who is from the Australia, New Zealand part of the world, than somebody who is 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 an activity in 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 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 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’ve 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. This really too much variations, so we need different ways to measure who's influential on a social networking site. Secondly, the authors found when they did some simulations, 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 had 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. [MUSIC]
