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TEDx Tuttle Presentation
 

TEDx Tuttle Presentation

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A presentation I gave at TEDx Tuttle in London on 17th September about how we'd tried (and often failed) to assess/address issues of "prestige" in social networks as part of our social media planning

A presentation I gave at TEDx Tuttle in London on 17th September about how we'd tried (and often failed) to assess/address issues of "prestige" in social networks as part of our social media planning

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  • Social media needs to be measured in different ways <br /> <br /> &#x201C;Civilization and its discontents&#x201D; where Freud talks about the conflict between the individual&#x2019;s desire to maintain their individuality and society&#x2019;s pressure to conform. <br /> <br /> I work as a marketeer; for years I&#x2019;ve been part of that pressure. <br /> <br /> Social Media presents -- express their individuality -- new set of unconscious pressures.
  • People are talking about your products, messages, about your ideas, about your policies.
  • Think about how many brands, NGOs and political organizations are trying to get you to do this. <br /> <br /> ethically grey.
  • In fact, I think it&#x2019;s naive.
  • Then we think about it as something we can brand. Or buy. <br /> <br /> I&#x2019;d say that social media is something that&#x2019;s emerging as a RESULT of the ease of publishing. <br /> <br /> TOO MUCH information.
  • Twitter <br /> <br /> Where do you hear about stuff FIRST? <br /> <br /> Unconscious subtle instantaneous decisions <br /> <br /> respect like love trust
  • Filters are almost fractally complex and individual. That&#x2019;s why they WORK. <br /> <br /> So here are some of the ways we&#x2019;ve tried to get around it.
  • how do I talk to as few people as possible to reach as many of the right people as possible? <br /> <br /> And so it&#x2019;s natural that we&#x2019;ve tried to take this model and apply it to the social media
  • people expose their social graphs on sites like Twitter and Facebook.
  • Here&#x2019;s mine.
  • I&#x2019;ve got 213 friends (actually I&#x2019;ve been pruning my friends)
  • But this is quite a narrow way of looking at things. You don&#x2019;t know anything about my friends. All you know is something like this...
  • Mat has some friends. <br /> <br /> This graph is a very simple EGONET
  • Here&#x2019;s how many friends my friends had back at the tail end of 2007 <br /> <br /> On the popularity model like this you&#x2019;d probably start off by talking to the people with most friends.
  • Here&#x2019;s how many friends my friends had back at the tail end of 2007 <br /> <br /> On the popularity model like this you&#x2019;d probably start off by talking to the people with most friends.
  • So there are better models -- like the Eigenfactor model -- where you look not just at HOW MANY, but WHO. <br /> <br /> Roughly speaking, the more POPULAR people that link to you, the more influence you have <br /> <br /> All sorts of things inc Google&#x2019;s PageRank works along these lines, so this is sort of &#x201C;Page Rank for People&#x201D;
  • So in the case of my profile, you&#x2019;d rather take on someone whose friend profile was more like THIS. Someone who knows more famous people as it were. <br /> <br /> But again, this is a bit simplistic.
  • Let&#x2019;s talk about Betweenness. Sometimes we talk about people being &#x201C;very connected.&#x201D; They tend to have high betweenness.
  • Here are four people&#x2019;s EGONETS back from the time when I began exploring. You can see who they know in common. <br /> <br /> Here&#x2019;s me. If you wanted to get an introduction to all four people, you could save time by just talking to me. <br /> <br /> I&#x2019;ve got what&#x2019;s called high BETWEENNESS in this network.
  • People with high betweenness are important. Without them, networks fall apart and messages don&#x2019;t get through.
  • In this network, the bright spots are the people with high betweenness. <br /> <br /> If you remove them
  • The network falls apart.
  • Here&#x2019;s another example. This time its Republicans and Democrats on Twitter. If you take out only two betweeners,
  • You get a world where the Republicans don&#x2019;t know what the Dems are saying and vice versa. <br /> <br /> Betweeners are people who span two or more worlds. Let&#x2019;s take a closer look at why there aren&#x2019;t MORE of them. It&#x2019;s because of a thing called Homophily.
  • homophily means (more or less) that people tend to hang around with and trust people who are like them <br /> <br /> And that people become more and more like the people with whom they hang around. <br /> <br /> It&#x2019;s a nice fat feedback loop.
  • Basically birds of a feather flock together. That also means that you can tell a man by the company he keeps. <br /> <br /> Which is interesting to us marketers. <br /> <br /> Humans are very GOOD at this sort of filtering. Or at least very PRONE to it. <br /> <br /> But how can we use it? Can we automate it?
  • Well -- it turns out we can. Up to a point. Here&#x2019;s a map of Twittering MPs. The colours tell you what their party affiliation is. <br /> <br /> Only, I didn&#x2019;t colour them by hand. I asked the computer to work out what the most likely cliques and factions were. <br /> <br /> It got one person wrong.
  • Derek Wyatt is actually Labour. But it&#x2019;s not bad for a small data set.
  • And here&#x2019;s the same exercise performed on the US Congress. We got six out of 46 wrong. Still feels pretty good. We&#x2019;re beginning to be able to tell something about someone based on nothing more than WHO THEY KNOW.
  • Because what we MIGHT be able to do is to find people who are more susceptible to certain kinds of message. <br /> <br /> People who are already experiencing real social pressure to make certain decisions. We might be able to REINFORCE that pressure, rather than try and INITIATE. Use their existing momentum. <br /> <br /> Sort of Kung Fu Marketing.
  • But the betweenness and homophily things also begin to explain why &#x201C;viral marketing isn&#x2019;t really viral.&#x201D; A lot of people who don&#x2019;t work in this area tend to have an oversimplified idea of how viral works.
  • 11:43 Media140
  • Most of the people who retweeted are already tightly linked -- recognized more than ONE name on the retweet schedule. There&#x2019;s a SOCIAL act going on here; not simply sharing information, but using the act of sharing information to reinforce their relationships w/in the wider network
  • So here&#x2019;s what I think. <br /> <br /> People are using their social networks to create filters that are impermeable to stuff we don&#x2019;t want to hear about. <br /> <br /> And as marketers we&#x2019;re looking for ways to get through those filters. <br /> <br /> But if we&#x2019;re going to understand how to do this, we need to find ways to measure things like respect, liking, loving, and trust. s

TEDx Tuttle Presentation TEDx Tuttle Presentation Presentation Transcript

  • Social Media and its discontents
  • Audience has an audience
  • Use people as a channel
  • Naïve
  • Not (just) about publishing
  • Filters
  • Bypass filters
  • Influence
  • Popularity
  • I have 213 friends
  • I am very popular
  • Don’t you want to be my friend?
  • I have 213 friends
  • Frequency distribution 20 15 10 5 0 10 60 90 460 2007
  • Frequency distribution 20 15 10 5 0 10 60 90 460 2007
  • Frequency distribution 20 15 10 5 0 10 60 90 460 2007
  • Eigenfactor
  • Frequency distribution 20 15 10 5 0 10 60 90 460 2007
  • Frequency distribution 20 15 10 5 0 10 60 90 460 2007
  • Betweenness
  • Homophily
  • Birds of a feather
  • Susceptibility
  • Why Viral Isn’t
  • exponential curve
  • diffusion curve
  • hiccough
  • hiccough attack
  • Our social networks are CLUMPY
  • London Twitter Festival Ends in Chaos as Crowd Clashes with Facebook Enthusiasts
  • Original Tweet: 0834hrs
  • Cascade: 1
  • Cascade: 2
  • Cascade: 3 (shows 2)
  • Cascade: 4 (shows 3)
  • Cascade: 5 (shows 4)
  • Friday, 11 Sep 2009 1115hrs
  • Friday, 11 Sep 2009 1115hrs
  • Initial tweet responsible for 2K visitors
  • Frequency distribution OTS/time 20,000 15,000 10,000 5,000 0 08:34 09:41 11:43
  • hiccough attack
  • Frequency distribution OTS/generation 30,000 22,500 15,000 7,500 0 Gen 0 Gen 1 Gen 2 Gen 3 Gen 4 Gen 5
  • hiccough
  • insufficient evidence for a real conclusion