The Famous Are Different From You And Me

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.

via

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).

12 November 2011 94 Permalink

You Are Who You Follow

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.

3 March 2011 47 Permalink

He’s Got A Gregarious Physiology

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.

29 December 2010 6 underpaidgenius Permalink

Content, Context, Conduit: It’s Not Who You Know, But Where You Know

The other night I was a participant on TummelVision, and one topic that came up was influence. I digressed and starting talking about betweenness, stating that being connected to people in very different ‘scenes’ is much more important than popularity in influencing people:

Stowe Boyd,  It’s Betweenness That Matters, Not Your Eigenvalue: The Dark Matter Of Influence

The most connected people in a social network — those with the highest number of incoming and outgoing connections — have high eigenvalues. These eigenvalues can be calculated — like Google’s PageRank algorithm — by weighting the value of each connection based on the eigenvalue of the originator.

But this research [ARVIX blog, Best Connected Individuals Are Not the Most Influential Spreaders in Social Networks] suggests that a different way to measure the centrality might be more useful in determining how much throw weight a person actually has. 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.

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.

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

I think this also is closely related to the notion of curation, which is being discussed a great deal these days, including the recent Business Insider Ignition conference:

Jared Keller, Who Do You Want Telling You What to Read?

I listened to John Borthwick of startup incubator Betaworks, Garrett Camp of discovery engine StumbleUpon, Patrick Keane of Associated Content, and Mark Josephson of the hyperlocal Oustide.in discuss the merits of algorithmic and crowdsourced modes of navigating the news. They’re all like me: they primarily discover content through a carefully curated Twitter feed, an RSS reader, or some other social news service.

So which is better? It’s usually a mix of the three. “Technology, math and algorithms are being used to refine and understand how people filter what they are looking at and how they read,” Borthwick said. “But mainly people read the voice of other people. There are new tools for getting there, so content production is being pushed into the pale, but most of these tools when they are used well are used to surface and filter, not compose.”

“In social media, everyone should be a content creator and curator,” Camp added. “StumbleUpon is trying to blend both worlds by asking for human input on thumbing stories up and down.”

In this sense, algorithms aren’t replacing editors or individual voices, but are used out of necessity: as the cost of creating content continues to drop, the sheer amount of content available to consumers has exploded. Algorithms are just there to lead the way. And sometimes those algorithms help us find content that, while not produced professonally, has an incredible amount of value.

So it’s not the content, which is the readage. Nor the context, as delivered by better metadata. And it’s not even the act of picking what is best, and passing it along, which is what most people mean by curation. What is really core are the actions taken to increase betweenness, by adding conduits to others who are well-connected and dropping ones that are less well-connected.

I am not sure how ‘carefully curated’ these folks’ twitter streams are, actually, but leave that aside. It is obvious that there is a very fast movement away from search and RSS, and toward Twitter as a source of readage for the world of media and mediaheads.

Instead of considering what we are doing as filtering, or curating, we should think about tinkering with our connections as a way to position ourselves in the network to maximize certain characteristics for the nodes we occupy.

Take betweenness, for example. I try to follow people who are connected to very well-connected people, where ‘well-connected’ does not mean popular, per se, but instead means connected to many people in different walks of life, different countries, different jobs.

The outcome of this tinkering with my connections is that I increase my betweenness: I shorten the number of links than connect me to the entire network, and the world.

Looking at it another way, I am also increasing my utility to those that follow me by increasing my betweenness: they are more likely to find unique insights or innovative ideas by following me, since I bridge many social scenes.

It’s a virtuous cycle: by adjusting my position in the network — by following and unfollowing — I improve the diversity and quality of readage I see, and by passing along the best of what comes along, my followers are better off. My actions improve their respective positions in the network too. And those of their followers, and so on. I am actually improving the entire network, by better positioning myself.

So it’s not the content, which is the readage. Nor the context, as delivered by better metadata. And it’s not even the act of picking what is best, and passing it along, which is what most people mean by curation. What is really core are the actions taken to increase betweenness, by adding conduits to others who are well-connected and dropping ones that are less well-connected.

And, no, there aren’t any algorithmic solutions out there for this. It would be handy if Twitter or Klout would offer up a betweenness value for a given user, or offer up some recommended connections based on the principle of positioning myself better in the network, not just because of topical relevance, or popularity. But they don’t.

Could you get on that guys?

Source: The Atlantic

4 December 2010 15 Permalink