April 25th & 26th
287 Kent Ave, Brooklyn, NY 11211
Abstract Submission Deadline: January 19th
What does it mean that digital technologies are increasingly a part of...
Duncan Watts, cited by Clive Thompson in Is The Tipping Point Toast?
Anything to undo the baloney that Gladwell has spread around.
Shea Bennett via AllTwitter
A recent study from Hubspot has determined that while highly-followed Twitter accounts share a lot of links, they converse less frequently than people who follow less than a thousand people.
Twitter accounts with a million or more followers tweet links three times more frequently than users with 1,000 followers or less, but only about 7% of their tweets are replies, compared with 17% for those with the smaller network.
I am not sure of the conclusion, that conversation doesn’t grow reach. These twitterers, with a million plus followers, are generally followed for something other than their curatorial and social skills: they are famous for their looks, acting, fiction, music, or some other notoriety. People follow them for completely different reasons than, say, following me.
Better to paraphrase F Scott Fitzgerald, and say that the famous are different from you and me.
I’d like to see a study about twitterers that are a/ not famous for something else, but b/ have amassed large following (more than 10,000 followers). What works for them might lead to better insights for the average joe who wants more followers.
The other findings are interesting, too: a lot of the social gestures in social media — likes, comments, and so on — don’t lead to more views. So, a person who has a dense network of involved friends might not be growing her network as a function of that network’s activities. This is a problem suitable for social network graph analysis, because all networks are not alike, and popularity isn’t the only way to measure impact (see It’s Betweenness That Matters, Not Your Eigenvalue: The Dark Matter Of Influence).
The new elite?
Bal Harbour Shops threw a party and only people with Klout influence scores of 40 or higher were eligible to come.
A bit general, since I have a Klout score above 40 (67 at the time of this post), but I am totally uninfluential in fashion (or I think I am).
Klout is now focussing on topic-related Klout scores, so in the future it could get fine-grained enough for that sort of distinction.
I am wagering that we’ll be seeing a lot of this at SxSW next year.
At the moment, this seem like the web equivalent of knowing the doorman at the popular clubs. In practice, it means you are connected online, are ready to mix and mingle, and ready to converse knowledgeably on a wide range of topics.
Klout groups me with Paul Kedrosky, Jay Rosen, and Umair Haque. Cool. People I truly admire, and who influence me.
Stephanie Rosenblum via NY Times
How does one become an influencer?
After analyzing 22 million tweets last year, researchers at Hewlett-Packard found that it’s not enough to attract Twitter followers — you must inspire those followers to take action. That could mean persuading them to try Bikram yoga, donate to the Sierra Club or share a recipe for apple pie. In other words, influence is about engagement and motivation, not just racking up legions of followers.
Industry professionals say it’s also important to focus your digital presence on one or two areas of interest. Don’t be a generalist. Most importantly: be passionate, knowledgeable and trustworthy.
Still, scoring is subjective and, for now, imperfect: most analytics companies rely heavily on a user’s Twitter and Facebook profiles, leaving out other online activities, like blogging or posting YouTube videos. As for influence in the offline world — it doesn’t count.
Mr. Azhar, of PeerIndex, calls this “the Clay Shirky problem,” referring to the writer and theorist who doesn’t use Twitter much. “He’s obviously massively influential,” Mr. Azhar said, “and right now he has a terrible PeerIndex.”
More broadly, Mr. Schaefer of Schaefer Marketing and others are concerned that we are moving closer to creating “social media caste systems,” where people with high scores get preferential treatment by retailers, prospective employers, even prospective dates.
We are moving into neo-tribalism, not some pan-democratic, egalitarianism. Why are people continuously surprised that the web is not a great leveler, but instead a redistribution of authority, and that not everyone will wind up with equal reputation?
PS I am still rooting for betweeness (degree of connection) instead of eigenvalues (popularity) to get down to the dark matter of influence (see It’s Betweenness That Matters, Not Your Eigenvalue: The Dark Matter Of Influence).
David Blei built an automated system to tag scientific articles, based on lexical analysis. He discovered that his tool would aggregate articles with similar terms, and based on the historical relationship, he wondered about how ideas spread. Put another way, we wanted to understand which papers spread important ideas, something often missing in the actual citations:
the Blei-Gerrish method may get closer to the real ebb and flow of scientific ideas and thus, in its way, offer a more scientific approach to science.Dr Blei found himself wondering if his method could yield any truly novel insights into the scientific method. And he thinks it can. In tandem with Sean Gerrish, a doctoral student at Princeton, he has now produced a version that not only peruses text for topics, but also tracks how these topics evolve, by looking at how the patterns in each topic bin change from year to year.
The new version is able to trace a topic over time. For example, a 1903 paper with the evocative title “The Brain of Professor Laborde” was correctly assigned to the same topic bin as “Reshaping the Cortical Motor Map by Unmasking Latent Intracortical Connections”, published in 1991. This allows important shifts in terminology to be tracked down to their origins, which offers a way to identify truly ground-breaking work—the sort of stuff that introduces new concepts, or mixes old ones in novel and useful ways that are picked up and replicated in subsequent texts. So a paper’s impact can be determined by looking at how big a shift it creates in the structure of the relevant topic.
In effect, Dr Blei and Mr Gerrish have devised an alternative to the citation indices beloved of scientific publishers. These reflect how often a particular publication or author is cited as a source by others. High scores are treated as a proxy for high impact. But a proxy is all they are.
Dr Blei and Mr Gerrish are not claiming their method is necessarily a better proxy. But it can cast its net more widely, depending on the set of documents fed into it at the beginning. Citation indices, which work only where publications refer to their sources explicitly, form a tiny nebula in the digital universe. News articles, blog posts and e-mails often lack a systematic reference list that could be used to make a citation index. Yet they, too, are part of what makes an idea influential.
Besides, despite academia’s pretensions to objectivity, it is as subject to political considerations as any area of human endeavour. Many authors cite colleagues, bosses and mentors out of courtesy or supplication rather than because such citations are strictly required. More rarely, an author may undercite. Albert Einstein’s original paper on special relativity, for example, had no references at all, even though it drew heavily on previous work. The upshot is that the Blei-Gerrish method may get closer to the real ebb and flow of scientific ideas and thus, in its way, offer a more scientific approach to science.
Marina Krakovsky: It's interesting that you include a story about hindsight from Paul Lazarsfeld, since he also co-wrote a book about influence. Would you speculate on what Lazarsfeld would think of your ideas about influencers?
Duncan Watts: That book [Personal Influence: The Part Played by People in the Flow of Mass Communications], with Elihu Katz (Free Press, 1955)] was a brilliant work, and probably the best book on social influence ever written. They had a fairly sensible view of what they called "opinion leaders". The problem with what they said is it got conflated with this other theory of diffusion and with what got labeled the "law of the few," which is not what they were talking about at all. Lazarsfeld and Katz define opinion leaders as the subset of society who consume a larger than average amount of media, and who make decisions about what's interesting and should be passed on, so they act as filters between the media and the rest of society. But even if you're talking about 10 or 20 percent of society, in America that's tens of millions of people, so it's not three or four people or a hundred people. So what I'm saying I think is consistent with what they said.
Marina Krakovsky: The most provocative and counterintuitive part of your book for me was your chapter "Special People," based on your research suggesting that buzz about cultural products is not spread through a predictable set of influencers.
Duncan Watts: Do you think that, really?
Marina Krakovsky: I do, particularly because you're responding to the by-now conventional wisdom that if you go through influential people, they will spread your message far and wide. And one of the pieces of evidence you muster against what Malcolm Gladwell in The Tipping Point [Little, Brown and Co., 2000] calls "the law of the few" is your small-world experiment, in which you replicate Stanley Milgram's famous letter-passing experiment on a massive scale using e-mail and show that there are really no hubs for transmitting messages, and that anybody in a network is as likely as anybody else to be an intermediary. But given that people might be reluctant to tap the most influential members of their network for a trivial favor like relaying an e-mail in an experiment, is the small-world study really a good model of how influence spreads?
Duncan Watts: No, I don't think the small-world experiment is a model of influence, and I don't claim that it is. But people think the small-world experiment works because of hubs, and I'm rebutting that claim on its own territory.
Marina Krakovsky: Along these lines you also describe your Twitter study, which, contrary to popular beliefs about viral marketing, showed that the overwhelming majority of messages don't get retweeted at all, and that previously influential Twitter users aren't reliably influential in the future. But Twitter seems as much a broadcast medium as mass media is. On Twitter you can follow somebody like Oprah directly just as you can tune into Oprah on TV, so I don't understand the distinction you make between Twitter and mass media.
Duncan Watts: I have a whole paper on this that didn't make it into the book. Twitter is interesting for studying influence because it spans the whole gamut—from entities that are true mass media, like CNN and The New York Times, through celebrities who would previously have had to use mass media instead of reaching people directly, all the way down to ordinary individuals. A lot of the debate about influencers has been mired in ambiguity because it's not clear who or what we're talking about, and the nice thing about Twitter is you can measure influence, and it has all these types of people in it, so you can compare them in an apples-to-apples manner. It does matter, on average, how many followers you have and how successful you've been in spreading your messages in the past, but it's a lot more random than intuition suggests. And then you can ask the real question, which is how to maximize the impact of your marketing dollar.
Mathew Ingram reports on the continuing efforts of various web services to decipher influence:
Mathew Ingram, The Race to Build a PageRank for the Social Web Continues
Both PeerIndex and Klout rank users based on data that comes from their Twitter, Facebook and LinkedIn accounts, although the two sites describe their rankings somewhat differently. Klout talks about overall “reach” and “amplification,” both of which are determined by looking at a user’s activity and how much impact it has on their social graph — whether their tweets are re-tweeted by others with influence, for example. PeerIndex says that it looks at a user’s activity in Twitter, Facebook and LinkedIn and then comes up with an authority rank for their expertise in eight topic areas, which it uses to create an influence “footprint” for each user.
As Klout and PeerIndex add more sources of reputation or influence data such as Quora to their rankings, the web moves closer to having a kind of Google PageRank for social activity, with all that implies. The big problem, as with Google search, is how to exclude the social equivalent of black-hat SEO and link spam, and how to determine what it is real influence and what is simply Justin Bieber-style popularity.
You are who you follow.
I continue to believe that these tools are motivated by a skewed model of what is going on online. It’s a sort of neo-classical economics viewpoint, where are individuals are considered as interchangeable, like replacement parts on an assembly line, or atoms banging into each other in a tea kettle of boiling water. And worse, they are considered in isolation, not as connected to others in a deep way.
We are social beings, and our social value is an emergent property of the network we are situated in, not a personality trait. We need to think about this issue in terms of position in the social network — who we follow and are followed by — not purely statistically.
Stowe Boyd, It’s Betweenness That Matters, Not Your Eigenvalue
Betweenness is a measure of how short are the chains that connects a person to the totality of the network. Like PageRank, betweenness is recursive: the people with the highest betweenness are likely to be connected to other people with high betweenness.
This means people are influential because they are connected to many influential people. But influence doesn’t seem directly linked to how many people you are connected to. It’s a function of being connected to others who have short chains to many other people with high betweenness. Or, looked at differently, betweenness is a measure of how many social circles, or social scenes, a person is connected to.
So, it’s not who you know, it’s where you know. It’s where you are situated in the network, and not just in the limited sense of how many immediate contacts you have.
The subtle, dark-matter mystery of social networks is that influence is oblique, and not easily determined by the sorts of tools we have today.
It is not your follower count, or who you follow, per se. But, instead, do you have short paths into other social scenes, both incoming and outgoing? That is the deep structure of being truly connected: bridging over different social scenes, acting as a conduit, a vector, a filter and amplifier for ideas good and bad, the best insights, and deadly viruses.
I maintain that the best predictor of an individual’s social value and the likelihood that their social activities will make an impact on others is who they are connected to, and in particular, who they follow. Following people from a diverse range of social scenes — different parts of the global social network — increases the likelihood that you will encounter new insights, new thoughts. And following more people who are connected themselves to diverse social scenes increases that likelihood. And that increases your social value to others.
You are who you follow.
Attempts to quantify our influence — our social value — without taking this into account are doomed to be a second order reflection of what is actually happening between us. It’s like trying to figure out why someone threw a rock into a stream by calculating the rock’s weight, trajectory, and the spread of the ripples. All very interesting, but it doesn’t get to why.
Adriaan Pelzer of Raak created someTwitter bots, that spewed interesting things at different periodicity, and discovered that Klout is broken.
The Klout Scores (the ugly)
For all practical purposes though, no matter how I look at it, Klout seems to be broken.
Consider the following Klout scores, for the four bots:
What’s wrong with this picture? To start off with, it should not really be possible for a bot to reach a Klout Score of 50 within 80 days merely by Tweeting random (yet entertaining) rubbish every minute, should it?
24 hours after the above klout scores were sampled, I took another set of samples, just to be sure:
Roughly the same result, except for huge fluctuations in transient metrics (see True Reach for Bot 1), which also seems a bit suspect. We can’t say for sure without knowledge of Klout’s exact algorithm.
The fact is, though, no matter how you look at it, unless Klout updates this aspect of their algorithm, in another 80 days Bot 1 could very well have the same Klout Score as @scobleizer!
Taking into account that many Twitter clients (like Hootsuite) and filter applications (like Datasift) are using Klout as a trusted way of filtering tweets, it means Klout will have to up their game on this one to stay in the game.
Yes, and the solution to fixing Klout is not just to figure out how to detect bots, but to discover a true metric of influence (see more on influence).
I have long maintained that the Dunbar Number — we supposedly can only maintain a small number of close relationships, and only remain connected to 150 people in total — isn’t a constant: it’s a variable. I have been on the look out for research that supports this premise, and something new has come to light.
Lisa Feldman Barrett, working with a team at Mass General Hospital in Boston, has new research that suggests that the size of the amygdala correlates strongly with the number of close friendships that people maintain:
Ian Sample, Social whirl of a life? Thank your amygdala
Researchers have found that part of the brain called the amygdala, a word derived from the Greek for almond, is larger in more sociable people than in those who lead less gregarious lives.
The finding, which held for men and women of all ages, is the first to show a link between the size of a specific brain region and the number and complexity of a person’s relationships.
The amygdala is small in comparison with many other brain regions but is thought to play a central role in coordinating our ability to size people up, remember names and faces, and handle a range of social acquaintances.
Researchers at Massachusetts General Hospital in Boston used magnetic resonance imaging (MRI) scans to measure the amygdalas of 58 people aged 19 to 83 and found the structure ranged in size from about 2.5 cubic millimetres to more than twice that.
As part of the study, each of the volunteers completed a questionnaire giving the number of people they met on a regular basis. They also commented on the complexity of each relationship. For example, one friend might also be a boss, meaning the person had to adapt their behaviour with the person depending on the nature of their encounter.
The team, led by psychologist Lisa Feldman Barrett, found that participants with larger amygdalas typically had more people in their social lives and maintained more complex relationships.
Those with the smallest amygdalas listed fewer than five to 15 people as regular contacts, while those with the largest amygdalas counted up to 50 acquaintances in their social lives. Older volunteers tended to have smaller amygdalas and fewer people in their social group.
Writing in the journal, Nature Neuroscience, Barrett’s team cautions that the finding is only a correlation, meaning they cannot say whether there is a causal link between the size of the amygdala and the richness of a person’s social life. However, previous studies with primates show that those that live in large social groups also have bigger amygdalas. “People who have large amygdalas may have the raw material needed to maintain larger and more complex social networks,” said Barrett. “That said, the brain is a use it or lose it organ. It may be that when people interact more their amygdalas get larger. That would be my guess.
“It’s not that someone with a larger amygdala can do things that someone with a smaller amygdala cannot do. People differ in how well they remember people’s names and faces and the situation in which they met them. Someone with a larger amygdala might simply be better at remembering those details,” Barrett added.
Barrett’s conjecture about brain plasticity is supported by many other studies, like Eleanor Maguire’s research on London taxi drivers, showing that gaining ‘the knowledge’ of the city’s streets leads to the growth of the hippocampus.
Relative to Dunbar’s Number, it seems that general brain plasticity is at work again: those that exercise the amygdala — by having more close relationships, or by putting themselves in the context of meeting and knowing more people — are likely to ‘exercise’ the amygdala, allowing them to broaden and deepen their social awareness about larger numbers of people. This suggests that ‘theory of mind’ is a deep skill, like martial arts or playing an instrument.
If you want to become more deeply invested in a larger number of relationships, you need to work at it, and use tools that make it possible to do it at all.
I maintain that streaming apps like Twitter serve amplifiers of our social awareness: our theory of mind. Just like cooked food allowed early hominids’ diet to change, freeing them from the requirement of chewing for hours every day, streaming apps make it possible to remain meaningfully involved with a larger number of people than formerly possible, and probably increasing the size of our amygdalas.
Of course, these are deep skills, and will require 10,000 hours of practice before we will have achieved mastery. Roughly ten years of practice for a few hours daily.
Maybe I should have my amygdala scanned.