memes gone viral

The other night, I overheard a really good question. After listening to an Obama campaign volunteer fervently discuss his recent mission and experience, the question was: "What are you going to do with all your Obama energy when the election is over?"

I liked the question because the energy in the political rhetoric right now is so tangible and in need of an outlet beyond next Tuesday. I started to think about where he could channel his energy, but also where all that energy came from.

As marketers have noted, the virality of Obama's campaign is admirable. They've captured a tough and large crowd in a conversation bigger than the election. Marketers praise the campaign for their mastery of the tools of engagement, for creating a transmedia conversation. 

Naturally, marketers are interested in that method-- in fact, they've virtually spawned a new genre of blogs devoted to "top 5-10" learnings from the Obama campaign. But the method is conversation, really. It's not simply the technical tools Obama has used as a vehicle for any particular message.

Biz Stone said in his press release on "Current Diggs the Election."

"Current is helping Twitter amplify the opinions, news, and trends that matter right now. Together, we're influencing more than media--we're evolving conversation."

The tools are evolving a conversation that's bigger than media.

I think the success of the Obama conversation is part tools, and part guidance as to what to do with the information, how to make sense of it, how to participate in the conversation. With just tools, I don't think his memes would be nearly as viral. 

"good" posts

Ideal conversation should be a matter of equal give and take, but too often it is all “take.” The voluble talker—or chatterer—rides his own hobby straight through the hours without giving anyone else, who might also like to say something, a chance to do other than exhaustedly await the turn that never comes.

-Emily Post, 1922

Not to beat a dead horse, but one thing that I glossed over in summing up the commentary on Peter Kim's Influence post was the implicit assumption that blogs recognized as "Influential" will have "good content." That is, we expect them to be both impactful and "good." You know as well as I that definitions of 'good' vary widely, but it makes sense to assume that after reading a post by an "influencer," you would want to walk away feeling like you just had a good conversation.

Being a good conversationalist is challenging... Sure there are rules, etiquette, and feedback to let us know how we're doing, but still no precise science. Furthermore, as Sunday's NY Times article on Sandy Pentland suggests, we're typically blind to feedback, or "Honest Signals"- unaware when we're dominating a conversation, interrupting, or have waning audience attention. 

Luckily blogs bring some of these cues to the forefront... and as we've seen, lack of attention quickly leads to decreased content production. 

So what makes for good content? Can we measure it?

Here's, my stab at defining and measuring "good content." Note: this is an untested, hypothetical algorithm to generate more engagement, as defined by empathetic commentary:

1. Authenticity

• This would have to be measured over time-- a measure of consistency in linguistic style to guage whether you're being true to what you know.  Perhaps we'd also look for a slightly higher usage of first person pronouns (I, me, my) to relay personal thoughts and opinions.
  

2. Economy of Language

• This could manifest via use of bullets and a low word count. Why? This not only generates intrigue, but shows your respect for your audience's attention. And conveys high status.
  

3. High Opinion: Question ratio 

• Might seem counterintuitive, but a colleague and I have been playing around with this idea. Questions don't seem to elicit a reaction without first offering fodder for the reaction: your opinion. Like the above 2, this too can be measured pretty easily with a basic text analysis program.
  

Just a hypothesis... I considered a few others, like unexpected associations and original thought, but I'm going to standby #1 in the name of anything having the potential to be interesting and "good." 

Anyone want to make a case for other variables? What else might predict loyal readership?

individual groups

Was talking to a friend this morning about his multiple identities... "Friend" has 2 Twitter accounts: 1 personal, 1 corporate. He was reflecting on the different ways he's perceived (or perceives he's perceived) when operating as each distinct identity.

"People give me the cold shoulder when I'm using my corporate account, they're surprised and delighted when I relay the same content from my personal one."

So the enterprise becomes a group of individuals, and we redefine the collective identity or personality of that enterprise.

What happens when an individual leaves? Do they take that piece of corporate personality with them? What's the impact on the enterprise? 

Reminds me of the relationship literature: how we are like overlapping venn diagrams of "me" and "romantic partner" and therefore suffer greatly upon break up. Part of you disappears. 

More important than departures, companies could face some serious identity crises in the near future as we figure out the balance between collective and individuals. Rolling people up into a 'greater sum' preserving their freedom to act as individuals... If not marriage counselors, maybe we can look to sports' coaches for advice.

influence influenza

Whether Influencer Listers are egoists or not, the comments on Peter Kim’s post last week questioning the self-promoting nature of said lists raised some interesting assumptions about what we expect from the construct of Influence.

To sum up the commentary:
• Influencer lists provide no value because they’re simply popularity lists.
• Influence is undefined and ambiguous.
• Influencer marketing is ineffective, or diluted because of size and the ambiguity of lists

I’ve blogged before about my dislike of measuring something for the sake of measurement. Specifically, I’ve been pretty harsh when people make magic formulas combining a hodgepodge of variables then call it Influence. In my opinion, these approaches go wrong for 3 reasons.

The formulas:
• Lack objectivity-- arbitrarily involve variables simply because they’re available (e.g. # friends)
• Lack reliability - incorporate variables that measure the same thing multiple times (e.g. friends on Facebook + followers on Twitter + connections on LinkedIn)
• Lack Validity - fail to show that they predict a meaningful behavior (e.g. “real influence,” sales, good content, etc.)

Without going into detail on psychometrics, I think others would agree there’s an abundance of digital breadcrumbs available to us… we have to start to show how they relate to meaningful constructs; influence, arguably, being one of them.

I think the call to arms today is mainly about validity: we need evidence that people are measuring what they’re trying to measure—that “influence” algorithms predict something meaningful (e.g. widget adoption?).

To be clear, our expectations for influence, influencers and influencer lists probably vary as widely as the ways they are being measured. Transparency will be key.

All this, and I still haven’t come down on the practice of influencer marketing…

media, medium, me

Last night I read a post by a particularly insightful guy questioning "to what extent news is news." He was deliberating movement in the market in relation to alleged news-- asking, to what extent is news embedded in the market...

With real-time technologies and more "social" ones at that, the role of "news" and/or "media" fluctuate.

I was thinking about this while listening to an outstanding presentation by Marcel LeBrun, CEO of Radian6 today. Marcel opened his talk with Marshall McLuhan's thoughts on "the medium as the message." Idea being, the medium, social technology in his case, has a larger societal impact than the messages contained in/on the medium. Marcel went on to talk about the richness of what he refers to as the "social phone," or social web in all its opportunities for participation.

Interestingly this same idea came up in David Matthia's comment in response to Peter Kim's post questioning the value of Influencer lists. Referring to Influencer lists as 'fad-ish', he suggests the real trend is in the influence of the medium-- in organizing and supporting groups (drastically simplified, but similar to what is discussed on David's blog, The Root Trend).

This is all particularly interesting to me because I don't necessarily think of the social web as a medium... Not that I'm the first to suggest this. I don't argue the richness, opportunity, or profound role of the technology in the markets or society, I just don't think of it as a medium. Still, I wonder about its role-- current and future.

insightful associations

When people strive for buzz, they typically want a lot.

Volume, quantity, attention.

Sometimes they yearn for a lot of positive buzz, but usually just a lot. I would argue the best buzz-- the best conversation-- is the discussion that alerts you to unique associations, people making "distant and unprecedented connections" with your brand, product, or issue of interest.

Turns out these types of connections define the neurological process of insight we exhibit during "aha moments."

Learning (about yourself, your brand, your 'topic') is not about being focused and paying attention to what you pre-determine to be the relevant details, it's about being open and receptive to fringe ideas and associations. Or not? When might listening to weak signals, distract you from the task at hand and do you wrong?

on texting, talking, and tweeting

There's an article about Jamie Pennebaker in today's Times-- how he's "breaking new linguistic ground and leading a resurgent interest in text analysis." True story. Jamie's a genius. And the timing is impeccable: with the proliferation of 'conversational technology,' there are so many more ways we can glean information about who people are and how they relate to themselves and others, simply by analyzing words. 

Another interesting opportunity is the ability to learn about the role of the medium: Twitter, Facebook, blogging, commenting, communities... Do we vary our style across medium-- do you come across differently as a blogger vs. an emailer? Does each channel fulfill a different need-- do you feel more supported participating in a community than commenting on a blog? Do you feel like you can get your point across better IMing than stopping by your colleague's cube?  

Start amassing data about your conversations, and if you're so motivated as to archive your every word, I'll happily do the text analysis and send you your results to see how your style varies with your self reports. 

Think about things like: how 'good' was the conversation? How connected did you feel to the person you were talking to? Did you communicate effectively? Why did you choose that form? I could go on... but it will become more interesting to hear what type of metadata you choose to archive. Please, let me know...

removing the "copy" from copyright; eyeballs from buzz?

Lawrence Lessig (of creative commons fame), had a nice piece in this weekend's WSJ, even if it mischaracterized his stance on piracy or the aboutness of his upcoming book, Remix.

His main contention is that copyright law has become corrosive in its inability to adapt to the new, creative use of digital technology (e.g. YouTube, Flickr, etc.). 

There's an interesting parallel in the way 'laws of measurement' have yet to evolve for the prolific creation and dissemination of content online. Still we find ourselves using a marketing measurement model. 

Lessig recommends the law should give up its obsession with "the copy"-- this immediately reminds me of the obsession to think about eyeballs exposed to buzz online-- views, GRPs, etc. that, I would argue, also should be abandoned.

An engaging conversation-- that which buzz usually represents-- engenders so much more than exposure. This is why I questioned the impact of mere views in fulfilling our attentional needs and spurring productivity (in creating content online). It seems that as technology transforms, everything related-- processes, metrics, laws-- should respond in kind. Most likely these will not be incremental enhancements, but transformations. 

Anyone else see a parallel?  

paying attention

Max made an interesting comment today, citing HP's Social Computing Lab's study, reinforcing his longstanding belief that attention is the currency of user contribution. It made me think about all the ways we pay-- and can be paid attention, and how we can convey it with varying degrees of depth.

We can visit, view, and look; or, we can interact, participate, engage... 

One of the most notable complements to any web technology is the ability to quantify it transparently. We have analytics for our every action, and as this study demonstrates, we're powerfully affected by our awareness of those metrics/ our performance. But what's interesting too is that most metrics consumed at face value lack depth (i.e. visits and views). 

What is the role of engagement in perceptions of attention? Does it matter? In the HP study, they used a Granger causality test to show that attention (as measured by YouTube views) CAUSED productivity (in the form of increased video uploads); and lack thereof actually led people to stop producing content. 

This is a pretty unbelievable testament to social perception. But more to the point, that level of behavioral change-- to produce more content or discontinue producing content-- was all a function of views. Not views and comments, dispersed views, return views, sentiment associated with views, influence of views, etc. Does engagement matter as much as we think?

As bloggers, which analytics are you most affected by? Mere quantity of visits? Furthermore how much does it affect you and your productivity?

sins of listening

I've always thought I had good hearing, but lately I've noticed (or have been told), my hearing is dwindling. FYI - my theory is I'm highly sensitive to noise (aka a superhearer) and thus have a lot of interference when it comes to discerning the signal. So naturally, I compensate for it by pretending I hear, doing some fuzzy (desperate) interpretation to make sense out of a blur of words and tone, and impulsively respond. I'm usually pretty off-the-mark...

I come from a brand monitoring world, a space where the potential for many variations of the above can easily happen. Importantly, I'm speaking from an analyst-as-consumer-of-data perspective-- could be a marketer, researcher, client, whomever it is attempting to listen to the data. Point being: ineffective listening is a sin. You should always query and re-query-- as many times as necessary-- so that the data can fully express itself. Data is, in some ways, more responsive than other conversational partners. 

As with consumer generated media, when the data universe is vast, participation is unregulated, and the best questions answered are unanticipated, the most frequent sins are probably acts of omission rather than commission. That is, it's not that you go wrong by acting on the data, but in not benefitting from what you could know if you had more effectively listened. 

I think clever interrogation is the formula for effective listening: a loop of listening, structuring, iterating and eventually analyzing. Which other effective strategies have you developed to better listen?

the new beast of editing

Yesterday I was obsessed with the idea of editing. Who are the new editors with democratized publication? What is their role? What are the varied ways editing manifests online, beyond the literal editing of a Wikipedia entry? A colleague offered up the idea of Twittering links to popular news as an example, be it an annoying one. 

I was thinking of it more in terms of collaborative filtering as a new mode of editing, a transformed mode. 

Is editing -- in this evolved state-- how the wisdom of the crowds prevails over the known dysfunction of groups (think: groupthink, loafing, etc.)? Is this how we achieve quality outcomes from crowdsourcing? If so, is the editor's need to 'get things right' self-serving? Is it even a need to 'get things right' or is more about filtering and mastery-- making sense of the world? 

I was ready to let it go, but then my mother called my attention to Tina Brown's newly launched Daily Beast a pastiche of traditional journalism and modern day online dialogue. David Carr's piece, aptly titled Editor of Note, Perched Online, says this:

With a slogan splashed across its home page promising rigorous editing of the culture for complicated times — “Read This Skip That” — the Beast is aiming to be a smaller, less chaotic version of the World Wide Web itself.

This style of 'rigorous editing' doesn't really seem to embrace the complicated times we are in (Note: purely technologically speaking). It seems like editing, online. I'd like to see Tina Brown achieve an evolved state of editing. But I think it will require more than 'sensibility'...

chasing engagement

Someone accused me of being like the Music Man when I suggested there are meaningful metrics that tap into the value in a conversation. Sadly, there is a lot of black magic out there in the form of widely variant algorithms for things like influence, engagement, and even less buzzy constructs like sentiment. But the constructs we're chasing exist; it's the methods that are misaligned. You don't necessarily need to add, or even use every variable available simply because you can.

People have deep interactions and make strong emotional connections. 

How do these manifest? Using a simple text analysis program, I did an experiment comparing 500 blogs in which people make a "definite recommendation" to a random sample of blogs (with a similar topical focus). Why? Recommendations have been hailed as the holy grail of word of mouth and even organizational performance (c.f. Net Promoter score).

If you look closely at the language people use around "definite recommendations" you see they are far more emotional, and as you might expect, more positive than negative. Interesting ecological validity there...

More interesting to me, recommendations are more intimate. People used more personal pronouns (I, me, my) indicating that they felt connected to the recommended items ("products") they were evangelizing.

And here's the clincher, they use more verbs than nouns: recommendations included more discussion of experiences with the products than discussion of objective attributes. Recommendations are experienced-based, not removed evaluations.

I don't interpret this as the precise litmus test that will give you the PH of a conversation, but it's a good example of the ability to tap into something valuable, relatively easily. Point being, I think something deeper usually lies beneath: subtle ways that people express deep emotional connections to brands, products, people, and the world in general. 

body choir: unexpected lessons for a healthy community

I was watching an old friend play some music last night and witnessed something that might offer some unexpected clues about what "health" could look like in an online community.

Scattered throughout the audience were friends of the musician who are also members of body choir. A friend told me that there's no talking allowed at body choir-- you speak, or rather sing with your body. This results in spontaneous, yet very fluid movements that you might have seen back in the days of EST groups. Eventually, they're dancing, completely in synch.

It used to amuse me, but last night I decided I was impressed by the symbiosis they achieve. 

There's this interesting process that sets off as various members start to internalize the music, make eye-contact with one another, exchange cult-like smiles, and eventually begin harmonizing their movements, seemingly rolling off one another. You can almost see a meme disseminate across the audience. 

How could an online community achieve this symbiosis-- each member internalizing the community's core values, generating that level of enthusiasm, and harnessing it into synchronized communications?

Can we study groups like this to derive an algorithm for the health of a community? A certain level of information, a vehicle to facilitate the transmission of that information, an optimal number of participants?

imposing structure; discovering structure

I was reading the Wikipedia entry on enterprise social software and was taken by an idea, which I think is actually a philosophy- my philosophy.  

"In contrast to traditional enterprise software, which imposes structure prior to use, this generation of software tends to encourage use prior to providing structure."

Use prior to structure... 

People always tell you this when you buy a house-- before putting in furniture, use it, walk around, figure out the traffic patterns, see which areas you and others naturally gravitate toward. Then, structure accordingly - make the furniture fit your lifestyle. 

This is my precise philosophy on how to approach new research constructs (e.g. Influence, Engagement, Authenticity), and data analysis in general. Map it out. Use your data and then divine structure. Down with Field of Dreams, 'If they come, it will build (grow)' seems a bit more apt today.

Surely I'm naive. When is it good to impose structure prior to use? People tell me schedules and plans work well in life. Is there anything in the enterprise that clearly calls for structure prior to use or can everything evolve to a place where structure emerges organically?

wwpjd? analyzing data, philip johnson style

I've been thinking a lot about what it would mean to analyze data from a glass house... All of a sudden reminds me of the time when Nick Denton opened up his blogging storefront on Crosby Street. 

Who can't relate to the situation when immersed in data, you yearn to channel various statisticians, or even your grad school advisor to figure out how best to proceed? Alternatively, have you ever been reviewing someone else's data, wanting desperately to point out something you think is interesting-- that you feel they should know, given your own forays in different data sets?

Several companies are getting into this idea of collaborative data analysis to help efficiently communicate important things as the amount of information proliferates. Early on, I reviewed one such impressive offering, Swivel.com. I was immediately taken with the idea of a community designed around data analysis-- not only the ability to annotate analyses, but access to wealths of metadata on who made the annotation, what else they've commented on, their background, expertise, etc. 

Today, Matt Hurst, a former colleague of mine blogged about DataDepot from Microsoft Research. Strangely, I can't access it anymore, but earlier I noticed there's a lot of room for engagement with the data here... commenting, annotating, easily adding new datasets. This could really transform the way we happen upon empirical insights and highlight complex relationships; and, in the end, happen upon emergent outcomes. No more ivory towers!

What, beyond privacy concerns, are the repercussions of analyzing data in a glass house? Seems like it could really motivate researchers to be productive...