Attention Economist

"Do you think we are in an Attention Economy?" Answered

2013-01-06T22:28:00.003-05:00

After reading a blog post by journalist Damien G. Walter, chorle asked on Yahoo Answers if we live in an attention economy:

My answer:
I think there has been a lot of disconnected writing about a phenomenon that has been poorly defined. In his 1971 speech, Herbert Simon said, "a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it." Later, in 1981, mathematical economist Sherwin Rosen provided some theoretical depth to this prediction in his paper, "The Economics of Superstars."

Several authors have argued that we have always lived in an attention economy, since social networks inherently involve the exchange of attention, which creates an economy. The relevant definition in this case is, "the management of the resources of a community, country, etc., especially with a view to its productivity." (Dictionary.com)

Michael Goldhaber was the first to use the term 'attention economy' and offered this definition (1997) when contrasting it to the information economy:

" ... if you have any particular piece of information on the Net, you can share it easily with anyone else who might want it. It is not in any way scarce, and therefore it is not an information economy towards which we are moving. What would be the incentive in organizing our lives around spewing out more information if there is already far too much? [...] There is something else that moves through the Net, flowing in the opposite direction from information, namely attention. So seeking attention could be the very incentive we are looking for."

Since his most concentrated period of publication in the late 90s, others have contested Goldhaber's definition (Aiyer Ghosh, 1997; Aigrain, 2006). While he has been working on a book for some time, several other authors pre-empted him, each with their own unique take on attention economics. In the end, what had the potential to be a well-defined concept ended up as a recycled meme meant to justify the general importance of social networking sites to the future of business.

If you need proof that there is competition for attention on the web, the long-necked (aka the long-tailed) power law distribution (Zipf's Law), turns up in plenty of research about attention allocation across the web (Huberman, 2002). Generally, attention will distribute unevenly across websites, with more attention concentrated among a tiny group of the total available sites. Also, any site that runs ads is tapping into the attention economy. Facebook, Google, LinkedIn and others sell ads where you might have to bid against competitors for the attention of the users who will be clicking.

I've talked about some of the future technologies that will help us measure, and therefore more easily commodify, attention. There are also some forward-looking descriptions of attention economies, such as Cory Doctorow's short-story, "Down and Out in the Magic Kingdom." It seems likely to me that our improving ability to track attention will result in an increase in its perceived value and the management technologies that help us invest and earn it.

TL:DNR - Attention is valuable and always has been, therefore attention economies have always existed within social networks, but the internet and mobile technology have made them much more tangible. We already sell and trade attention in the form of ads or promoted links, but advancing attention-tracking tech will permit much greater levels of sophistication in the design of future attention economies.

References:

Aigrain, P. (2006). Diversity, attention and symmetry in a many-to-many information society. First Monday, 11(6)

Aiyer Ghosh, R. (1997). Economics is dead. Long live economics! A Commentary on Michael Goldhaber’s “The Attention Economy”. First Monday, 2(5)

Goldhaber, M. H. (1997). The Attention Economy and the Net. First Monday, 2(4).

Huberman, B. A. (2002). Zipf’s law and the Internet. Glottometrics, 3, 143–150.

Rosen, S. (1981). The Economics of Superstars. The American Economic Review, 71(5), 845–858.

Simon, H. (1971). Designing Organizations for an Information-Rich World. Computers, Communication, and the Public Interest.

A Brief History of Attention Economics & Future Attention-Tracking Technology

2012-07-19T12:59:00.000-04:00

Why Attention Economics?

We live in a gold rush, with digital entrepreneurs building businesses on mountains of users and trying to mine (then monetize) every second of attention invested on their site. We will see continued hyper-specialization as new social networks emerge and seek out audiences with untapped needs. As we improve our attention management software, we may see a freezing in network growth as global available attention pools decrease. What happens then?

Mass reciprocity is one trend I find particularly fascinating on social networks. The #FollowBack is used across networks as a signal of willingness to reciprocate any proffered social link. I used this tactic for gaining followers until recently, when I pulled a Chris Brogan and unfollowed over 7000 users. It occurred to me that I was diluting my attention to the point where I was sacrificing individual relationships in favour of amplifying of my own content. So I limited myself to follow no more than 1,000 accounts at any one time.

This is what happens when you unfollow 7000+ people in one day (TwitterCounter.com).
Y-axis = total number of followers

I wanted to understand what kind of differences you might observe in populations that distribute attention with low selectivity compared to those with higher selectivity. After some research, I found that a number of scholars had already examined some of the theoretical and philosophical underpinnings of an 'attention economy,'

Defining Attention Economics
Since many of the links I've provided within this post have their own definitions of an attention economy, I'd like to clarify how I will be using the terms. Where a node represents an object or individual that can receive and/or invest attention:

Attention Economy = any network where attention is exchanged between more than one node
Attention Economics = the study of the flow of attention within a network and the systems that organize and redistribute this attention between nodes

It follows that almost anyone who uses a social network is an attention economist, making daily decisions on how they will consume the attention of their audience.

The Attention Economists

Nobel Laureate, Prof. Herbert Simon
Wealth of information = poverty of attention
Carnegie Mellon Library Archives

There are several researchers and scholars that have examined the flow of attention from an economic perspective, inspired by a 1971 speech by Nobelist Herbert Simon. who identified the scarcity of attention as a major driving force in a new economy:

"... a wealth of information means a dearth of something else - a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it comes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it."

The rest of Simon's article focuses on the importance of early adoption of computing technology as a recipe for success. It would seem his foresight was flawless, although maybe not on the timescale he recommends in the paper.

Michael Goldhaber
First to use 'Attention Economy'
Goldhaber.org

Michael Goldhaber’s The Attention Economy and the Net was one of the first clear elucidations of the internet as a space to trade attention, although he first to used the term 'attention economy' (despite many sources falsely citing Prof. Herbert Simon) in a talk that was published in Education and the American Dream in 1989. Jonathan Beller followed suit when he wrote about an "informal economy [of] (attention)" in a footnote of a paper in 1994 and would much later publish the book The Cinematic Mode of Production: Attention Economy and the Society of the Spectacle.

Jonathan Beller
Credit: Trebor Scholz

Georg Franck
Dom Research Lab

In 1999, Georg Franck added to Goldhaber's definition by elucidating the importance of de-materialization and virtualization as driving forces towards an attention economy, since they stand to decrease the demand for a range of careers that focus on physical creation of objects, freeing up more "attention workers."

The first book about the attention economy by Davenport and Beck was published in 2002, but the (very over-priced) text reads like a guide to the importance of earning and organizing attention, with outdated website examples and some limited descriptions of predicted "attention-monitoring technologies."

Alex Iskold
Founder GetGlue
His LinkedIn Profile

While they've assembled an interesting set of references, they completely neglected the ongoing conversation that followed Goldhaber's first article about attention economics in First Monday and Telepolis, and only gave him two scant mentions in their preface! Maybe if they had written a section on crimes of the attention economy, they would have recognized their own citation-skulduggery.

Alex Iskold’s four articles at ReadWriteWeb from 2007 outline examples of websites functioning as attention economies. Iskold also pointed out that we need to develop attention standards and "each silo that captures a users attention needs to provide an interface to access it."

He predicted consumers would eventually demand open access to their attention data, although the movement has been lacklustre and so has the response from the big players in social media.

Prof. Richard Lanham
Author of Economics of Attention
California Literary Review

On the heels of Beller's book, English Professor Richard Lanham published The Economics of Attention: Style and Substance in the Age of Information in 2007. Although similar to the Davenport and Beck book in many regards, it reads like high level guide to creating attention-earning content strategy on the internet. Goldhaber didn't particularly like this book (maybe because Lanham also failed to reference his work?), and makes it very plain, with his review entitled, "How (Not) To Study the Attention Economy." At one point, Goldhaber brings up the fact that neither he nor his fellow attention economists had any formal training in economics:

"The fact that three such disparate people hit on more or less the same idea at similar times might just be conincidence, or it might suggest there is something to it. What does it mean then that none of us are professional economists? Perhaps it was necessary. If you are proficient in a discipline, you have learned to reject thoughts that are outside the box."

However, I would argue that the importance of attention and its organization has been written about extensively in many different fields, but an effective synthesis has been lacking. Later, in the same paper, Goldhaber writes:

"If attention economics were mathematizable, it would certainly require a rather different mathematics."

This contradicts what he wrote in his 1997 paper in the online journal First Monday, What's the Right Economics for Cyberspace?:

"Attention is not standardized and measurable like a commodity; you can't give or trade away all the attention you have accumulated, as would be possible with currency and the goods bought with it, nor can you barter attention for something else."

This paper was written as a reply to Rishab Aiyer Ghosh, then-Managing Editor of First Monday, and his critique of Goldhaber's work, where he rightly points out that, "even attention can be monetised - the whole advertising industry is grounded on this."

Prof. Josef Falkinger
University of Zurich Profile

Some of this 'mathematization' has already been published by Josef Falkinger, a career researcher and Dean of economics at the University of Zurich. His 43-page working paper suggests that anything that allows the sender to increase the strength of their signal within a network will decrease the overall reach of any individual sender, since users will begin to focus their attention on increasing their own local diversity. Said differently, an attention economy will encourage users to shift their attention away from celebrities or major nodes within a network (non-reciprocal relationships) as they recognize the value and importance of finding connections that guarantee returns on their attention investments (reciprocal relationships).

Prof. Bernardo Huberman
Prof. Consulting as HP Research Fellow
HP Profile Site

Since then, there have been a number of researchers who have studied the flow of attention while incorporating mathematical, with HP's Senior Research Fellow Bernardo Huberman standing out as a clear authority in the field. Just shortly after Goldhaber published his original article on attention economics in 1997, Huberman was first author on a paper entitled, "An Economics Approach to Hard Computational Problems" and has co-authored a number of papers relating to information exchange within networks.

There have also been several examples of applied attention economics research. Seriosity Inc. patented an attention economy for messaging within a business environment and more recently Fan and Zhao proposed a system for modelling collaborative activities using attention as a core consideration. Also, Kawano et al. revealed that they were working on a prototype for simulating attention exchange on Twitter.

While I've neglected to mention lots of really interesting research and blogging, I hope to cover more in future blog posts.

The State of Attention-Tracking Technology

In a discussion I had recently with Vincent Longueville of @AttEcon, he pointed out:

Although previously I might have disagreed, it's clear to me now that it's pointless to make any distinction: every website or social media account represents a node within a network that may earn attention from or spend attention on other nodes. As technology to track this flow improves, the systems that organize and represent attention transactions should similarly improve.

From Clicking to Gestural Interaction to Gaze Control

Currently, we track attention as the bounce rate, number of unique visitors or views, and many websites and search engines sell attention to advertisers using cost-per-click or cost-per-impression metrics. Tablet devices such as the iPad offer a new range of gestural data for tracking interactions and this will likely mature as we develop gloves that allow Minority Report-like gestural control of technology.

Google's new Glasses are being rolled out to the public (see ProjectGlass, above), bringing us one step closer to gathering data about where a user is directing their gaze. Google has already patented eye-tracking technology for Project Glass, suggesting this could very well be part of their first prototypes that will be handed out to developers early in 2013 for $1,500 a pair (UPDATE: not part of them).

Eye-tracking headgear
Toni Järvenpää

Right now, for anywhere between $7-40K, you can invest in a hardware/software combo system that will track a user's gaze as they stare at a target, such as a website. Our increasingly efficiency-focused workplaces will likely require us to adopt Project Glass-like visors or wear display-ready contact lenses in the near future, which would allow employers to track how their employees invest their work time. While this might seem restrictive, I think many people would trade transparency about they spend their work time for increased flexibility on working hours and location. Why even have a specific location for work when your supervisor can simply tune in to live spectacle-mounted cameras or review a daily video digest of your work?

There will be plenty of people opting-out for privacy reasons, but as our internet population develops increasingly varied perspectives on the sharing of personal data, it seems inevitable to me that we will value attention-tracking software for a variety of reasons. Assuming you develop standards about how to measure and publish those attention investments, you can then begin transparently tracking where you invest, and your content receive investments.

The Future: Limited Emotional Categorization with Affective Computing

Attention has many layers and determining the intensity of how it is being spent will be important to furthering our ability to track it. Affective computing is one potential future solution that currently involves two fields of sub-research, as suggested by Xiangyang Li and Qiang Ji:

Sensory data and measures: information regarding your physiology and speech that are sampled and compared to other datasets in order to try to infer emotional state
Graphical facial and gestural data: information from photos and videos taken of you can be compared to other data sets to try to infer emotional state

Rosalind W. Picard pointed out the major shortcoming of affective technology: "No one can read your mind." However, in this review paper she goes on to outline how universal recognition of emotional states by any technology was irrelevant, provided that technology could learn to infer emotional state based on past individual data.

"The experiments in identifying underlying sentic state from observations of physical expression only need to demonstrate consistent patterning for an individual in a given perceivable context. The individual's personal computer can acquire ambient perceptual and contextual information [...] to identify autonomic emotional responses conditioned on perceivable non-emotional factors. [...] The priorities of your agent could shift with your affective state."

If we had reliable affective computing technology, we could potentially pick out a spectrum of standardized emotions and try to use 'personal agents' to try to set up an individualized intensity metric based on your typical responses, but the system's success would be largely dependent on the agent software.

The Further Future: Jacking into Neurons

In 2009, University of Wisconsin Biomedical Engineers developed an EEG cap that allowed them to tweet by brain activity. Prof. Justin Williams:

"And what your brain does is, if you’re looking at the ‘R’ on the screen and all the other letters are flashing, nothing happens. But when the ‘R’ flashes, your brain says, ‘Hey, wait a minute. Something’s different about what I was just paying attention to.’ And you see a momentary change in brain activity.” "

See the quicktime video over at their website. Brain-computing interfaces (BCI) have been a staple of science fiction, but they don't appear to be on our short-term scientific horizons. The conclusion section of a brain-computer interface review by 11 researchers in the Journal of Neuro Engineering:

"Why is it that, in all these years of development, not more progress has been achieved? We believe that in each of the steps of the BCI cycle major improvements are needed. Yet, expectations concerning BCI's potential use easily runs high, especially in the popular media. It is important, both for the research community as well as for potential users, to make a clear distinction between currently feasible and potentially possible applications in order to prevent unrealistic expectations."

So unfortunately, we're probably not getting nanites in the brain, or caps for thought-controlled devices in the near future.

One day we might tap into the kinds of data we receive from brain-scanning equipment (such as the EEG, MEG, PET and fMRI) in a much more mobile, and permanent manner, we could then begin to define the intensity, or completeness, of attention as it is being spent.

Cory Doctorow
Activist and Author
His website, Craphound.com

Consider the way that Cory Doctorow describes his version of attention currency in his (free!) science fiction novel, Down on Out in the Magic Kingdom:

"Dan moved to block the bedroom door. “Wait a second,” he said. “You need rest.”I fixed him with a doleful glare. “I’ll decide that,” I said. He stepped aside.“I’ll tag along, then,” he said. “Just in case.”I pinged my Whuffie. I was up a couple percentiles—sympathy Whuffie—but it was falling: Dan and Lil were radiating disapproval. Screw ’em."

This set-up he describes is based not only on a regular neuronal jack to measure where Dan and Lil were paying attention, but also the precise type of attention and emotions associated with that attention. Doctorow's Whuffie seems to have some complicated conversion system that generates positive transference of Whuffie for positive emotions, and loss of Whuffie when receiving negative emotions. Taking into account the earlier section of this post that discusses brain-computer interfaces, I doubt even the most optimistic futurists would predict this kind of technology on the foreseeable horizon.

Thus, I would respectfully disagree with Michael Goldhaber when he says:

"... attention is not absolutely quantifiable and never will be."

As I wrote about earlier in this post, we are already on the path to developing improved attention-tracking technology, meaning we are coming closer to measuring attention in an 'absolute' manner. I think Aiyer-Ghosh summarized the semantic argument best:

"To conclude, given the validity of the core economic concept of scarcity, "information economy" and "knowledge economy" are inappropriate to describe the "new" economy - if taken at face value rather than as convenient placeholders. "Attention economy" is closer to the truth, being tied to the truly scarce resource of cyberspace, its human inhabitants."

In the same paragraph, Aiyer-Ghosh recommends we just stick with 'economics,' but I think this seriously risks confusing a wider readership. Falkinger's work demonstrates some of the major fundamental divergences between our transactional economy and an attention economy, it seems to make sense to me that Goldhaber's original term does indeed deserve its distinction.

Recently, I’ve seen social media speculators refer to a number of alternative economies such as, the ‘data,’ ‘knowledge,’ ‘reputation,’ and 'intention' economies. However, I would argue that most of these would have to rely on some measurement of attention in order to accurately determine value. Any system that measures the flow of attention and creates an algorithm or interactive system to transform it into another value, at the very least, will benefit from an understanding of this flow.

A system designed to value data or knowledge might, for instance, try to reflect the usefulness or popularity of a particular item. Since certain items will inevitably receive different amounts of attention in a crowd-sourced scoring system, their value would be directly related to the amount of attention received. One might argue that Google's search is a knowledge, or data economy, since it creates a ranking system for all information surrounding a particular keyword. However, the information it uses to generate these scores is based on attempts at measuring attention flow: backlinks, provided they don't get picked out by Google's Penguin algorithm as paid links or for 'keyword stuffing' their website. In Google's opinion, paying or using content-devaluing tactics to elevate your perceived attention revenues from their algorithm is a crime of the attention economy.

Conclusion
Increasing immersion in cyberspace will provide us with endless opportunities to understand ourselves from the waves of data left in our wakes. Whereas marketing used to be most about creativity and intuition, it will become a data-heavy field in need of informaticians and in pursuit of the most quantifiably sound methods for selling products. I'm not convinced that an zero-sum game, with organizations endlessly chasing new attention-getting memes and adverts is sustainable. More 'users' are levelling up to digital native status and the marketplace already offers a number of attention-sensitive strategies for website and product design, represented well by the global shift to simpler, mobile-friendly, user-centric design. With some of the aforementioned advancements in technology, I think we'll see the researchers that study the flow of attention become absolute necessities for many organizations.

References

Aiyer Ghosh, R. (1997). Economics is dead. Long live economics! A Commentary on Michael Goldhaber’s “The Attention Economy.” First Monday, 2(5), 2-5.
Abecassis, D., Cheng, H., Philips, M., Read, L., Reeves, B., Roy, S., and Atherton, R. (2011). US Patent: 7,918,388 B2. United States Patent to Seriosity Inc.
Bang-Jensen, J., and Gutin, G. (2007). Digraphs Theory , Algorithms and Applications.
Delong, J. B., and Froomkin, A. M. (2000). Speculative microeconomics for Tomorrow’s Economy. First Monday, 5(2), 1-20.
Davenport, T. and Beck, J. C. (2001). The Attention Economy: Understanding the New Currency of Business. Harvard Business Press.
Falkinger, J. (2003). Attention Economies. CESifo Working Papers, Category 9(1079).
Fan, S., and Zhao, J. L. (2012). Attention-Aware Collaboration Modeling. E-Life: Web enabled-convergence of commerce, work and social life.
Franck, G. (1999). The Economy of Attention. Telepolis. Retrieved May 17, 2012, from http://www.heise.de/tp/artikel/5/5567/1.html
Goldhaber, M. H. (1997). The Attention Economy and the Net. First Monday, 2(4), 1-12.
Goldhaber, M. H. (1997). What’s the Right Economics for Cyberspace? First Monday, 2(7), 1-7.
Huberman, B., Lukose, R., and Hogg, T. (1997). An Economics Approach to Hard Computational Problems. Science (New York, N.Y.), 275(5296), 51-4.
Kawano, Y., Kishimoto, Y., and Yonekura, T. (2011). A Prototype of Attention Simulator on Twitter. NBiS 2011.
Lanham, R., and Kaplan, M. (2008). The Economics of Attention. New York.
Li, X., and Ji, Q. (2005). Active Affective State Detection and User Assistance, 35(1), 93-105.
Picard, R. W. (1995) Affective Computing. MIT Media Laboratory Perceptual Computing Section Technical Report, (321).
Simon, H. (1971). Designing Organizations for an Information-Rich World. Computers, Communication, and the Public Interest.
van Gerven, M., Farquhar, J., Schaefer, R., Vlek, R., Geuze, J., Nijholt, A., Ramsey, N., et al. (2009). The brain-computer interface cycle. Journal of neural engineering, 6(4), 041001. doi:10.1088/1741-2560/6/4/041001

An Assessment of Facebook's Flaws and Future

2012-05-21T12:33:00.003-04:00

On Wednesday (May 16^th), I was interviewed by CBC writer Andre Mayer about my opinions on Facebook's future. This post contains the points I used to justify my predictions that Facebook's on a road to failure in the long run and some opinions regarding their recent software changes.

The Walled Garden: Forced Reciprocity

Facebook built its empire on reciprocal relationships; there are no one-way friendships on Facebook (or at least until subscriptions there weren’t, more about that later). This is not reflective of human relationships – all of us have a variety of relationship types, and if you’re a content creator with any audience you have never invested attention in, you are the target of a one way relationship. Twitter and Google+ have recognized the value of permitting subscription based relationships, both of them offering a basic ‘follow’ or ‘circle’ subscription relationship, but then have additional features for organizing and listening.

"Forced reciprocity in social network games" - Yvette Wohn

When coupled with Facebook’s limit of 5,000 friends per personal account, deciding when to transition to a Facebook Page has been a necessary consideration for any ascending celebrity. However, on September 14^th, 2001, Facebook launched a subscription feature for personal accounts, meaning that having a page was, from that point forward, unnecessary for personal brands. The new Timeline means the differences between personal accounts and Pages is now lesser, which may be Facebook’s attempt to remedy their about-face on reciprocity.

Ignoring the technical details, Facebook’s culture was not built on asymmetry so just offering the option is not going to change the user culture. If they had gone full out and forced users to adopt an asymmetrical model by changing all ‘friendships’ into reciprocal following relationships, they might have been able to stem Google+'s flood of new users. If they were to try this kind of a switch now, it would seriously alienate a rapidly diversifying internet population that has come to see Facebook as the personal network to share with close friends – not to mention the danger it would spell for their already poor reputation for respecting privacy.

I wonder though, whether this might have been the only possible model that would have allowed Facebook to rise to prominence. If they had used a Twitter-like broadcasting model back in 2005 when they launched, would they have made it this far? I’m of the opinion that Zuckerberg chose the reciprocal model because the social consequences of destroying the link of Friendship (one of Facebook’s most valuable assets) were sufficiently high as to discourage unfriending. This is reflected in their choice of small, difficult to find icons for the ‘Unfriend action’ – they have no interest in helping you unfriend your Facebook connections.

As the importance of developing a personal brand becomes embedded in our internet culture, forced reciprocity, or as Dwyer reminded me in his article, “the Walled Garden model,” will prove a barrier to expanding your personal network. I predict a globall shift in awareness of reputation data that will encourage users to increasingly delete data, limit content and look for a new, pristine network where they can build their reputation profile.

Social Channelling: Where’s the ‘World Broadcast’ Mode?

Facebook is focused around the newsfeed – there is no Facebook-wide search tool, no way to see what multiple users are saying about something particular. Some analytics companies have begun offering keyword-based data retrieval for public Facebook personal accounts and Pages, but nothing exists that is cheaply available to the average user. This will hurt Facebook in the long run since etiquette surrounding content creation and interaction is changing rapidly; Twitter and Google+ are acclimatizing their users to receiving specific interactions from outside of their personal connections. The network growth and diversification potential of open-forum social networks has greater staying power than the ‘personal network,’ IMO, but Google+ already offers clear content privacy filters with the option to post in ‘world broadcast’ mode. If you can do both at once, doesn’t that make the most sense?

Facebook has done a poor job of helping people organize themselves around topics; Pages and Groups are simply not enough. Their recent launch of Interest Lists are a way for people to package Pages, but seem a little like a shot at the rapidly growing Flipboard. It’s not difficult to conclude these tactics insufficient, for the simple reason that Interest Lists are not as accessible on mobile on Flipboard’s visually-pleasing application. Facebook needs to leverage its massive userbase to beat out its competitors in pushing breaking news by offering a manner to broadcast to everyone and to scan these broadcasts sent to everyone.

It seems to me that we are unlikely to see this feature anytime soon. While a nifty way for companies to borrow memes for advertising purposes, it would be such a shift away from the ‘personal’ focus they’ve taken for the Timeline and might upset their userbase. While I would not put it past Facebook, I suspect if they launched an open broadcast mode there would be overwhelming negative feedback, which they will be more sensitive to after sharing control with their new investors.

Moving Too Slowly to Mobile

Facebook’s mobile apps are severely lacking in functionality and have yet to properly adapt to the value provided by the Timeline. Moreover, there is significant inconsistency in user experience for pages, decreasing the value for businesses looking to engage users on-the-go. No question that Facebook is long overdue for an app re-design and it would be great if they rolled in some of their recent acquisitions too.

The Instagram acquisition has been declared a protective move by several tech analysts, since Facebook has yet to properly leverage their massive photodatabase on phones. Also, their recent announcement of a Facebook App Store smacks of ‘we know we need to be more mobile-friendly.’ However, as Tim Bradshaw and April Dembosky pointed out in the Globe and Mail, Facebook is not the owner of a mobile device from which to push their platform and apps. It will be a nice change from the current eye-sore, but won’t improve their prospects on mobile.

While Facebook has been making acquisitions and big news, Twitter released news that didn’t make as much of a buzz as I thought it should have:

“Like Twitter for iPhone and Twitter for Android, mobile.twitter.com is fast, like a native mobile app; it uses one-third less bandwidth than the previous iteration. We’re rolling out this new mobile web experience starting today, and will continue to make Twitter the most accessible way to connect with the world, even with the weakest signals and the simplest devices.”

Facebook has recently been losing users in North America and will probably continue to earn better growth in markets where access to internet is limited. However, since mobile devices may well be the first internet experience for users in these markets, and there is no guaranteeing the quality of connection in regions, I think Twitter’s decision to streamline their software is perfectly timed. As Facebook continues to decline under its own weight in North America, Twitter is now poised to draw from their remaining source of user growth with their more accessible, simple interface.

The Facebook Timeline as an Aggregator

There are still many unsatisfied niche populations in many different corners of the internet and for the next several years I think we’ll see continued hyperspecialization as the cost of producing social networks decreases and new groups grow and proliferate. Throughout this expansion period, there will be an increasing need for aggregator services that bring together content from multiple social networks into a single profile.

The number of recent ‘landing page’ services (ie: About.Me, Glos.si, Flavours.me) are an indicator of the insufficient job that the major social networks are doing at aggregating content from the rapidly proliferating small, specialized networks. The Timeline is evidently an attempt to reposition Facebook as the aggregator for all your social content and their marketing around “friction-less sharing” is another attempt to dispel (justifiable) user fears about broadcasting clicks inadvertently via passive sharing. Suggestions that Mark Zuckerberg is an advocate for radical transparency might explain their strategy, although IMO actively encouraging seamless sharing will always get construed as forcing openness on users whenever something goes wrong. Facebook’s privacy controls and definitely some of the best out there, but coupled with Facebook’s history and the high effort investment for users who have many apps, they become an easy scape-goat whenever a user makes a mistake with their sharing.

Open Access for Attention Data?

The major product that many of these services peddle is our attention (my 'What is Attention Economics?' post). They use data regarding our attention that they measure primarily in clicks and page-residence time, then they sell the additional information they can glean from these clicks to advertisers. Nearly 1 billion people believe that giving away access to their attention a social network is worth the ability to use their site. I think that as attention-tracking technologies improve and users desire an increasing amount of access to their personal data, social networks will be forced to go open access. In this kind of future, as Gerd Leonhard put it, every form of media will be widgetized and used for “narrowcasting.” I would extend his prediction by suggesting we will begin to focus our interactions via personally-controlled spaces (so we can control our own attention data), releasing information selectively to our multitude of widgets, sharing ourselves in increasingly specific manners to targeted audiences. It would probably look a lot like Google+.

Caveats

Important to note: Facebook has a massive lead. With the huge backing their IPO will provide and their already sizeable userbase, they have a very healthy lead on their competitors. I don't think we'll see Facebook disappear for at least another 5-10 years, but we may see them start to lose more ground in North America and Europe in the next 2-3 years. If Facebook's public status changes its tactics and pits it against the interests of its userbase, they could lose large chunks of their audience to variations on the 'Quit Facebook' memes. Or maybe, they're on top of it? I'd be interested in hearing your views on its future in the comments, or on any of my widgetized channels.

Which is worth more: an Automatic, or a Manual Retweet?

2012-03-14T20:28:00.002-04:00

The Retweet: Some Historical Perspective

The Retweet was originally an abbreviation adopted by the Twitter community to indicate attribution in cases of resharing a tweet. Twitter eventually listened to its users and coded the automatic Retweet into its interface. The automatic Retweet prevented modification of the Tweet and simply transplanted the Tweeter’s profile photo and text directly onto the Retweeter’s profile, like a donation of attention from one account to another. Many users saw this as an assault on their nomenclature of choice – why could Twitter not have favoured their preferred method of resending information? :

[Optional: Retweeter’s additional text] RT @username [Original tweet text and links]

Tweetdeck, a yellow-branded Twitter monitoring software represented by a regal raven, protected the original citation style by building it into their interface. When you clicked to Retweet a tweet using Tweetdeck, it would bring you to a darkened version of the tweet that could be automatically retweeted or edited in the original Retweet format:

RT @username: [Original tweet text and links]

Time passed and Tweetdeck’s userbase rose, to the point where it could justify opening a directory for its fans. Twitter (probably) fumed with jealousy as it noted the rise in users posting via Tweetdeck – then purchased the company. There was some speculation that Twitter may have bought out Tweetdeck to keep it out of Ubersuite’s hands, but I think Twitter had equal motivation to satisfy greater demand for more powerful monitoring software and didn’t want to keep losing so many users to a secondary service. Also, they needed an alternative to their excessively simplistic mobile interface that would provide more monitoring power on-the-go. Since then, they have completely rebranded the web and desktop versions of Tweetdeck, substituting the raven with a Twitter bird. Interestingly, the Twitter bird for the main web interface is a simple blue with no outline, whereas the bird to represent Tweetdeck is the same shape, but black with a blue background. Shade upgrade! *High Five*

Initially, as a part of the rebrand Twitter slashed the Tweetdeck directory and destroyed the option to perform manual Retweets. In place, they forced users to 'Quote Tweet' and imprison the Retweeted text and username in quotation marks. Besides being very visually jarring, quotation marks tend to throw off certain monitoring software and do not permit the range of communication styles many users prefer. The MT, or modified Retweet, is used to represent an altered quotation, which is more clunky to accomplish using the 'Quote Tweet' function. Obviously, Twitter wants to discourage users from changing what the original author said. Not necessarily a bad thing, but it comes into conflict with their users' creative attempts to beat the tight word limit.

Reaction to the 'Quote Tweet' feature, via Matt Silverman on Mashable

The last bastion of RT-nostalgia lay in the TweetDeck mobile app. Despite an update since Twitter's acquisition of Tweetdeck, the mobile app still allows you to edit an automatic Retweet and will insert the RT before the @username. Symbolically, Twitter has yet to force Tweetdeck mobile app to drop the yellow raven in favour of the blue bird, although I'd wager the next major update might change that.

After you select, 'Edit Retweet,' this is what appears on mobile Tweetdeck. Just like old Twitter. Via Graham Charlton.

More recently, Twitter updated their TweetDeck Web and Desktop versions to include an 'Edit & RT' option, which mimics the old manual Retweet style, and have done away with the option to 'Quote Tweet.'

For the purpose of this thought experiment...

From this point forward, it is important to note that whenever I refer to the manual RT, I am ONLY considering cases where the Retweeter does not add or change any of the content, simply adds the text, "RT @Username" before the text contained in the tweet. Considering RTs with additional text in front, or modified tweets would complicate the discussion.

What Influences the Probability to Retweet?

Let's assume the body of people you follow on Twitter is, for the most part, unchanging. You will start to become familiar with the tweets and their associated branding as they populate your timeline. By 'associated branding,' I mean the essential parts of a tweet where a user is likely to focus their gaze as they scan through their personal timeline, such as the profile photo, the profile name and username region.

My initial hypothesis when I started thinking about this was that section a) would be the most visually powerful, followed by c), then b). My justification: faces and images are eye-catching and novel in a landscape of black and coloured link-text. We may skip over a the Profile Names as we skim text, but I hypothesized that the image, followed by the text in the Tweet would be the two points of major eye-focus on the mobile and web versions of Twitter. After some relatively simple research, I found two YouTube videos-worth of anecdotal data that strongly suggest the text within a tweet receives the most significant visual focus.

Now, let's define different aspects of a tweet that might influence the probability to both consume and Retweet a tweet:

Visual Attraction: The probability someone will choose to look at a particular tweet while scanning their personal Twitter timeline.
Content Attraction: The probability a user will be willing to invest attention in a tweet based on various textual factors. These include, but are certainly not limited to: personal opinions of the user(s) tweeting or mentioned in the tweet, keywords and content of the tweeted text, timing of the tweet, appropriateness of the Tweet, relevance of the Tweet, etc. Worth noting that defining what is and what is not a donation of attention could be problematic when trying to examine data from multiple users.

If we were looking to calculate the probability any single follower of a Retweeter was going to invest attention by allowing their gaze to rest on any particular Retweet for a sufficient amount of time, we could define it as the Probability for Attention Investment.

Watching You, Watching Twitter

Since no eye-tracking studies to date have been conducted regarding tweets, the best I could find were these Youtube videos that detail that ocular focus of an "expert" Twitter user in the process of navigating the website. From the Tobii Eye Tracking Youtube Channel, take a peek. You might be surprised.

Notice how, when scanning down a series of tweets in his/her personal timeline, the viewing user looks primarily at the text of the tweets and devotes almost no eye focus to the profile photos at the left hand side of a tweet. Now, you can compare it to a video below of a "beginner" Twitter user. You'll notice that not much changes when you scan the timeline:

Does this mean that the content interest is the majority contributor to the PAI? In my opinion, that's unlikely, simply because the data we are missing with current eye-tracking technology most likely favours the Visual Attraction component of aforementioned equations. In their 2008 review paper about the future of eye tracking in research for online searching, Lorigo et al. admits:

"... it is important to note that eye tracking does not tell us how much users perceive in their peripheral field; to the best of our knowledge, nearly no literature studying peripheral vision exists from which we can effectively extrapolate to the context of online searching."

Since there is minimal visual data to draw from the small, 48x48 pixel (on the web interface) thumbnail that represents each user, it's more likely that they become badges for quick identification in visual scans, also known as saccades. In the earlier gaze-tracking videos, you can see that the expert performs a saccade vertically along the line of profile images, suggesting she or he is scanning for a particular image (begins at 45 seconds).

Confounding Factor: "Retweeted by [Username]"

It's worth noting that the "Retweeted by [Username]" text that appears below any tweet in the web or mobile version is a confounding factor; this would be a key for users to visually identify and potentially filter Retweets.

How do we react to novel stimuli?

To determine whether it is more advantageous to have your own profile pic (manual retweet) vs. someone else's profile pic (automatic retweet), you can look at some research that has been done regarding our reactions to novel stimuli. Park, E. Shimojo and S. Shimojo published in PNAS regarding the "roles of familiarity and novelty in visual preference judgements."

The 22 subjects in their experiments exhibited no preference for familiar or novel geometric figures, but preferred novel landscapes and familiar faces. One important question; when so many people use profile pictures that are mostly-landscape, or where their faces take up a tiny portion of the thumbnail, can these be considered faces when shrunk for a tweet, or would our brain compute them as geometric features/landscapes?

A good quotation found in Jeremy M. Wolfe's review paper on "asymmetries in visual search" outlines the relevant Treisman Hypothesis:

"... it is easier to detect a deviant among standard stimuli than to find the standard stimulus hiding among deviants (Treisman and Gormican, 1988)."

The review goes on to mention work by Shen and Reingold (2001) where they demonstrated that, while the relative familiarity or the novelty of the user's target for the visual search was not important, having distractors that were familiar was important.

Said differently, if you are targeting 'good' content and all tweets can potentially be considered distractions, their results applied to this scenario might suggest that it would be more efficient to search for a visually novel tweet (automatic Retweet) than another tweet that were less so (manual Retweet). However, their symbol and character-based experiments are very simple compared to the visually complicated attention-trap of the Twitter Timeline and there is nothing to suggest that the studies mentioned here are necessarily transferable to Twitter.

Saccade (Visual Search) Speed Matters

Personally, when I use Twitter, I feel like I am absorbing significant visual information from profile photos in tweets, even though I am not focusing on them. This could be information absorbed peripherally, or I might be deluding myself.

I would guess that as the speed of the saccade increases, the importance of easily recognizable visual elements as anchors, or places to land, increases. For instance, when you're scrolling quickly on an iPhone, it might be more likely you will seize on a familiar photo than an unfamiliar one, then use this as a landing point to begin scanning at a slower speed. Maybe at slower scanning speeds, novel stimuli pop-out more?

Influence Metrics: Automatic RT Wins

If you take the pragmatic approach of looking at the relative contribution of automatic and manual Retweets to current influence metrics, the automatic Retweet is preferable. I have a feeling that when Klout drinks from the Twitter Firehose of data, it interprets manual RTs and automatic RTs differently. While it's impossible to divine the equation for their proprietary metric, I'm guessing automatic RTs are worth more since they are considered 'amplification.' Manual RTs probably do not count towards this aspect of the Klout score.

Secondly, consider that when you send a manual RT, you are placing yourself as a new hub for amplification of the retweetee's content. Had you automatically RT'ed their tweet, you may have spurned many other people to take the same action, increasing the personal brand exposure for this individual. If your manual RT gets retweeted many times over, you receive more quantitative brand attention since your face and your name feature more prominently than the content creators'/curators' single username mention - it is much easier to click on the owner of a tweet than the usernames contained within. Also, it seems unlikely to me that the automatic RTs of your manual RT will figure into the content creator's Klout score, or other reputation metrics. In an attention economy, would we consider this a form of theft? I think so, and have adopted a personal policy of only performing a manual RT when I feel I have text to contribute that alters the content significantly.

The Most Important Point: Reputation Donation

When you perform a manual RT, you increase the probability that your reputation will be a deciding factor in whether or not to donate extra attention to this tweet. If someone who follows you feels negatively about your personal brand, having your face on the tweet might change the probability they will be willing to interact with it. If the user who views your Retweet and the Retweetee are not familiar with one another, I'd suggest the importance of reputation drops precipitously and content interest as well as visual attraction become the primary components of consideration. The argument on the other side, however, might be that some people would feel less comfortable donating attention to users who they are less familiar with. The latter seems substantially less likely to me, considering how far certain tweets can travel even when sent by users who do not have the network strength to provide that kind of amplification.

Conclusion

No matter how you slice it, the automatic Retweet is a better choice. Additional eye-tracking research will be important to reveal more about how users interact with the Twitter interface and may provide interesting answers to some of my hypotheses. But, unless the content you're adding generates something new, when you auto-RT, you are stamping your brand on something you didn't create and potentially robbing the creator of the ability to track further message amplification.

References

Lorigo, L., Haridasan, M., Brynjarsdóttir, H. Xia, L., Joachims, T., and Gay. G. 2008. "Eye Tracking and Online Search: Lessons Learned and Challenges Ahead." Journal of the American Society for Information Science and Technology. 59(7):1041–1052, 2008
Park, J.,
Shimojo, E., and
Shimojo, S. 2010. "

Roles of familiarity and novelty in visual preference judgments are segregated across object categories." PNAS. 107(33): 14552-14555

Shen, J. and Reingold, E.M. 2001. "Visual search asymmetry: The influence of stimulus familiarity and low-level features." Perception and Psychophysics. 63(3): 464-475.
Treisman, A. and Gormican, S. 1988. "Feature Analysis in Early Vision: Evidence From Search Asymmetries." Psychological Review. 95(1): 15-48.
Wolfe, J.M. 2001. "Asymmetries in Visual Search: An Introduction." Perception and Psychophysics. 63(3): 381-389.

The Attention Economy and Influence Metrics: #Scio12 Presentation

2012-01-25T15:31:00.000-05:00

A Storify of the presentation I gave with Nature Publishing Group's Lou Woodley at ScienceOnline 2012. Unfortunately, blogger does not allow hosting of embedded Storify stories on dynamic view blogs.