Aardvark employees would get the questions from beta test users and route them to users who were online and would have the answer to the question. This was done to test out the concept before the company spent the time and money to build it, said Damon Horowitz, co-founder of Aardvark, who spoke at Startup Lessons Learned, a conference in San Francisco on Friday.

“If people like this in super crappy form, then this is worth building, because they’ll like it even more,” Horowitz said of their initial idea.

At the same time it was testing a “fake” product powered by humans, the company started building the automated product to replace humans. While it used humans “behind the curtain,” it gained the benefit of learning from all the questions, including how to route the questions and the entire process with users.

This is a really good idea, as I’ve argued before on this blog and in a chapter for developers of mobile health interventions. What better way to (a) learn about how people will use and experience your service and (b) get training data for your machine learning system than to have humans-in-the-loop run the service?

My friend Chris Streeter wondered whether this was all done by Aardvark employees or whether workers on Amazon Mechanical Turk may have also been involved, especially in identifying the expertise of the early users of the service so that the employees could route the questions to the right place. I think this highlights how different parts of a service can draw on human and non-human intelligence in a variety of ways — via a micro-labor market, using skilled employees who will gain hands-on experience with customers, etc.

I also wonder what UIs the humans-in-the-loop used to accomplish this. It’d be great to get a peak. I’d expect that these were certainly rough around the edges, as was the Aardvark customer-facing UI.

Aardvark does a good job of being a quite sociable agent (e.g., when using it via instant messaging) that also gets out of the way of the human–human interaction between question askers and answers. I wonder how the language used by humans to coordinate and hand-off questions may have played into creating a positive para-social interaction with vark.

Instead, I want to relate this discussion to my continued interest in how activity streams and other information consumption interfaces affect their users’ beliefs and behaviors through the availability heuristic. In response to some comments on his first post, Stutzman argues that people overestimate the degree to which content once public on the Internet is public forever:

So why is it that we all assume that the content we share publicly will be around forever?  I think this is a classic case of selection on the dependent variable.  When we Google ourselves, we are confronted with what’s there as opposed to what’s not there.  The stuff that goes away gets forgotten, and we concentrate on things that we see or remember (like a persistent page about us that we don’t like).  In reality, our online identities decay, decay being a stochastic process.  The internet is actually quite bad at remembering.

This unconsidered “selection on the dependent variable” is one way of thinking about some cases of how the availability heuristic (and use of ease-of-retrievel information more generally). But I actually think the latter is more general and more useful for describing the psychological processes involved. For example, it highlights both that there are many occurrences or interventions can can influence which cases are available to mind and that even if people have thought about cases where their content disappeared at some point, this may not be easily retrieved when making particular privacy decisions or offering opinions on others’ actions.

Stutzman’s example is but one way that the combination of the availability heuristic and existing Internet services combine to affect privacy decisions. For example, consider how activity streams like Facebook News Feed influence how people perceive their audience. News Feed shows items drawn from an individual’s friends’ activities, and they often have some reciprocal access. However, the items in the activity stream are likely unrepresentative of this potential and likely audience. “Lurkers” — people who consume but do not produce — are not as available to mind, and prolific producers are too available to mind for how often they are in the actual audience for some new shared content. This can, for example, lead to making self-disclosures that are not appropriate for the actual audience.

1. This might not be the case, see Michael Zimmer and this New York Times article.
2. Why don’t people do this in the first place? Many may not be aware of the feature, but even if they are, there are reasons not to use it. For example, it makes any participation in topical conversations (e.g., around a hashtag) difficult or impossible.
3. Or at least this control would have to be via Twitter, likely before archiving: “We asked them [Twitter] to deal with the users; the library doesn’t want to mediate that.”

“Supertaskers did a phenomenal job of performing several different tasks at once,” Watson says. “We’d all like to think we could do the same, but the odds are overwhelmingly against it.” (Wired News & Science News)

The researchers, Watson and Strayer, argue that they have good evidence for the existence of this individual variation. One can find many media reports of this “discovery” of “supertaskers” (e.g., Psychology Today). I do not think this conclusion is well justified.

First, let’s consider the methods used in this research. 100 college students each completed driving tasks and an auditory task on a mobile phone — separately and in combination — over a single 1.5 hour session. The auditory task is designed to measure differences in executive attention by requiring participants do hold past items in memory while completing math tasks. The researchers identified “supertaskers” as those participants who met the following “stringent” requirements: they were both (a) in the top 25% of participants in performance in the single-task portions and (b) and not different in their dual-task performance on at least three of the four measures by more than the standard error. Since two of the four measures are associated with each of the two tasks (driving: brake reaction time, following distance; mobile phone task: memory performance, math performance), this requires that ‘’supertaskers” do as well on both measures of either the driving or mobile phone task and one measure of the other task.

There may be many issues with the validity of the inference in this work. I want to focus on one in particular: the inference from the observation of differences between participants’ performance in a single 1.5 hour session to the conclusion that there are stable, “trait” differences among participants, such that some are “supertaskers”. This conclusion is simply not justified. To illustrate this, let’s consider how the methods of this study differ from those usually (and reasonably) used by psychologists to reach such conclusions.

Psychologists often study individual differences using the following approach. First, identify some plausible trait of individuals. Second, construct a questionnaire or other (perhaps behavioral) test that measures that trait. Third, demonstrate that this test has high reliability — that is, that the differences between people are much larger than the differences between the same person taking the test at different times. Fourth, then use this test to measure the trait and see if it predicts differences in some experiment. A key point here is that in order to conclude that the test measures a stable individual difference (i.e., a trait) researchers need to establish high test-retest reliability; otherwise, the test might just be measuring differences in temporary mood.

Returning to Watson and Strayer’s research, it is easy to see the problem: we have no idea whether the variation observed should be attributed to stable individual differences (i.e., being a “supertasker”) or to unstable differences. That is, if we brought those same “supertasker” participants back into the lab and they did another session, would they still exhibit the same lack of performance difference between the single- and dual-task conditions? This research gives us no reason that expect that they would.

Watson and Strayer do some additional analysis with the aim of ruling out their observations being a fluke. One might think this addresses my criticism, but it does not. They

performed a Monte Carlo simulation in which randomly selected single-dual task pairs of variables from the existing data set were obtained for each of the 4 dependent measures and then subjected to the same algorithm that was used to classify the supertaskers.

That is, they broke apart the single-task and dual-task data for each participant and created new simulated participants by randomly sampling pairs single- and dual-task data. They found that on this analysis there would be only 1/15th of the observed ‘’supertaskers”. This is a good analysis to do. However, this just demonstrates that being labeled a “supertasker” is likely caused by the single- and dual-task data being generated by the same person in the same session. This stills leaves it quite open (and more plausible to me) that participants’ were in varying states for the session and this explains their (temporary) “supertasking”. It also allows that this greater frequency of “supertaskers” is due to participants who do well in whatever task they are given first being more likely to do well in subsequent tasks.

My aim in this post is to suggest some challenges that this kind of approach has to face. Part of my interest in this is that I’m quite sympathetic to identifying stable, observed differences in behavior and then “working backwards” to characterizing the traits that explain these downstream differences. This  exactly the approach that Maurits Kaptein and I are taking in our work on persuasion profiling: we observe how individuals respond to the use of different influence strategies and use this to (a) construct a “persuasion profile” for that individual and (b) characterize how much variation in the effects of these strategies there is in the population.

However, a critical step in this process is ruling out the alternative explanation that the observed differences are primarily due to differences in, e.g., mood, rather than stable individual differences. One way to do this is to observe the behavior in multiple sessions and multiple contexts. Another way to rule out this alternative explanation is if you observe a complex pattern of behavioral differences that previous work suggests could not be the result of temporary, unstable differences — or at least is more easily explained by previous theories about the relevant traits. That is, I’m enthusiastic about identifying stable, observed differences in behavior, but I don’t want to see researchers abandon the careful methods that have been used in the past to make the case for a new individual difference.

Watson, Strayer, and colleagues have apparently begun doing work that could be used to show the stability of the observed differences. The discussion section of their paper refers to some additional unpublished research in which they invited their “supertaskers” from this study and another study back into the lab and had them do some similar tasks measuring executive attention (but not driving) while in an fMRI machine. They report greater “coherence” in their performance in this second study and the previous study than control participants and better performance for “supertaskers” on dual-N-back tasks. But this is short of showing high test-retest reliability.

Since little is said about this work, I hesitate to conclude anything from it or criticize it. I’ve contacted the authors with the hope of learning more. My current sense is that Watson and Strayer’s entire case for “supertaskers” hinges on research of this kind.

References

My thinking on persuasion profiling is very much in BJ Fogg’s footsteps, since he has been talking about persuasion profiling in courses, lab meetings, and personal discussions since 2004 or earlier.

Just yesterday, I came across this transcript of BJ’s presentation for an FTC hearing in 2006. I was struck at how much it anticipates some of what Maurits and I have written recently (more on this later). I’m sure I watched the draft video of the presentation back then and it’s influenced me, even if I forgot some of the details.

Here is the relevant excerpt from BJ’s comments for the FTC:

Persuasion profiling means that each one of us has a different set of persuasion strategies that affect us. Just like we like different types of food or are vulnerable to giving in to different types of food on a diet, we are vulnerable to different types of persuasion strategies.

On the food example, I love old-fashioned popcorn, and if I go to a party and somebody has old-fashioned popcorn, I will probably break down and eat it. On the persuasion side of things, I know I’m vulnerable to trying new things, to challenges and to anything that gets measured. If that’s proposed to me, I’m going to be vulnerable and I’m going to give it a shot.

Whenever we go to a Web site and use an interactive system, it is likely they will be capturing what persuasion strategies work on us and will be using those when we use the service again. The mapping out of what makes me tick, what motivates me can also be bought or sold, just like a credit report.

So imagine I’m going in to buy a new car and the person selling me the car downloads my credit report but also buys my persuasion profile. I may or may not know about this. Imagine if persuasion profiles are available on political campaigns so that when I visit a Web site, the system knows it is B.J. Fogg, and it changes [its] approach based on my vulnerabilities when it comes to persuasion.

Persuasive technology will touch our lives anywhere that we access digital products or services, in the car, in our living room, on the Web, through our mobile phones and so on. Persuasive technology will be all around us, and unlike other media types, where you have 30-second commercial or a magazine ad, you have genres you can understand, when it comes to computer-based persuasion, it is so flexible that it won’t have genre boundaries. It will come to us in the ordinary course of our lives, as we are working on a Web site, as we are editing a document, as we are driving a car. There won’t be clear markers about when you are being persuaded and when you are not.

This last paragraph is about the “genrelessness” of many persuasive technologies. This isn’t directly on the topic of persuasion profiling, but I see it as critically relevant. Persuasion profiling is likely to be most effective when invisible and undisclosed to users. From this and the lack of genre-based flags for persuasive technology it follows that we will frequently be “persuasion profiled” without knowing it.

Google Scholar is often my first stop when finding research literature (and for general search), and it has this feature — just click “Cited by 394″. But it is not very useful when your starting point is highly cited. What I often want to do is to do a keyword search of the papers that cite my highly-cited starting point.

While there is no GUI for this search within these resultsin Google Scholar, you can actually do it by hacking the URL. Just add the keyword query to the URL.

This is the URL one gets for all resources Google has as citing Allport’s “Attitudes” (1935):

http://scholar.google.com/scholar?cites=9150707851480450787&hl=en

And this URL searches within those for “indispensable concept”:

http://scholar.google.com/scholar?hl=en&cites=9150707851480450787&q=indispensable+concept

In this particular case, this gives us many examples of authors citing Allport’s comment that the attitude is the most distinctive and indispensable concept in social psychology. This example highlights that this can even just help get more useful “snippets” in the search results, even if it doesn’t narrow down the results much.

I find this useful in many cases. Maybe you will also.

Chess Wars was being rejected after the six week wait [because] the bubbles in its chat rooms are too shiny, and Apple has trademarked that bubbly design. [...] The representative said Stump needed to make the bubbles “less shiny” and also helpfully suggested that he make the bubbles square, just to be sure.

One thing that is quite striking in this situation is that it is at odds with Apple’s long history of strongly encouraging third-party developers to follow many UI guidelines — guidelines that when followed make third-party apps blend in like they’re native.¹

It’s important to not read too much into this (especially since we don’t know what Apple’s more considered policy on this will end up being), but it is interesting to think about how responsibility gets spread around among mobile applications, services, and devices — and how this may be different than existing models on the desktop.My sense is that experienced desktop computer users understand at least the most important ways sources of their good and bad experiences are distinguished. For example, “locomotion” is a central metaphor in using the Web, as opposed to the conversation and manipulation metaphors of the command line / natural language interfaces and WIMP: we “go to” a site (see this interview with Terry Winograd, full .mov here). The locomotion metaphor helps people distinguish what my computer is contributing and what some distant, third-party “site” is contributing.

This is complex even on the Web, but many of these genre rules are currently being all mixed up. Google has Gmail running in your browser but on your computer. Cameraphones are recognizing objects you point them at — some by analyzing the image on the device and some by sending the device to a server to be analyzed.

This issue is sometimes identified by academics as one of source orientation and source equivocality. Though there has been some great research in this area, there is a lot we don’t know and the field is in flux: people’s beliefs about systems are changing and the important technologies and genres are still emerging.

If there’s one important place to start thinking about the craziness of the current situation of ubiquitous source equivocality is “Personalization of mass media and the growth of pseudo-community” (1987) by James Beniger that predates much of the tech at issue.

1. I was led to think this by a commenter on TechCrunch, Dan Grossman, pointing out this long history.

The workshop began with a short keynote from Patricia Greenfield, a psychology professor at UCLA, about the costs and benefits of media multitasking. Greenfield’s presentation struck me as representing as an essentially conservative and even alarmist perspective on media multitasking.

Exemplifying this perspective was Greenfield’s claim that media multitasking (by children) is disrupting family rituals and privileging peer interaction over interaction with family. Greenfield mixed in some examples of how having a personal mobile phone allows teens to interact with peers without their parents being in the loop (e.g., aware of who their children’s interaction partners are). These examples don’t strike me as particularly central to understanding media multitasking; instead, they highlight the pervasive alarmism about new media and remind me of how “helicopter parents’” extreme control of their children’s physical co-presence with others is also a change from “how things used to be”.

Face-to-face vs. mediated

The relationship of these worries about mobile phones and the allegedly decreasing control that parents have over their children’s social interaction to media multitasking is that mediated communication is being privileged over face-to-face interaction. Greenfield proposed that face-to-face interaction suffers from media use and media multi-tasking, and that this is worrisome because we have evolved for face-to-face interaction. She commented that face-to-face interaction enables empathy; there is an implicit contrast here with mediated interaction, but I’m not sure it is so obvious that mediated communication doesn’t enable empathy — including empathizing with targets that one would otherwise not encounter face-to-face and experiencing a persistent shared perspective with close, but distant, others (e.g., parents and college student children).

Family reunion

Greenfield cited a study of 30 homes in which children and a non-working parent only greeted the other parent returning home from work about one third of the time (Ochs et al., 2006), arguing — as I understood it — that this is symptomatic of a deprioritization of face-to-face interaction.

As another participant pointed out, this could also — if not in these particular cases, then likely in others — be a case of not feeling apart during the working day: that is, we can ask, are the children and non-working parents communicating with the parent during the workday? In fact, Ochs et al. (2006, pp. 403-4) presents an example of such a reunion (between husband and wife in this case) in which the participants have been in contact by mobile phone, and the conversation picks up where it left off (with the addition of some new information available by being present in the home).

Next

I’m looking forward to the rest of the workshop. I think one clear theme of the workshop is going to be differing emphasis on costs and benefits of media multitasking of different types. I expect Greenfield’s “doom and gloom” will continue to be contrasted with other perspectives — some of which already came up.

References

Ochs, E., Graesch, A. P., Mittmann, A., Bradbury, T., & Repetti, R. (2006). Video ethnography and ethnoarchaeological tracking. The Work and Family Handbook: Multi-Disciplinary Perspective, Methods, and Approaches, 387–409.

1. Which also means I’m multitasking, in some senses, through the whole conference.

In his new book, How We Get Along, philosopher David Velleman puts it this way:

At one extreme, I have a way of interpreting myself, a way that I want you to interpret me, a way that I think you do interpret me, a way that I think you suspect me of wanting you to interpret me, a way that I think you suspect me of thinking you do interpret me, and so on, each of these interpretations being distinct from all the others, and all of them being somehow crammed into my self-conception. At the other extreme, there is just one interpretation of me, which is common property between us, in that we not only hold it but interpret one another as holding it, and so on. If my goal is understanding, then the latter interpretation is clearly preferable, because it is so much simpler while being equally adequate, fruitful, and so on. (Lecture 3)

That is, one way my self-views can be efficient representations is if they serve double duty as others’ views of me — if my self-views borrow from others’ views of me and if my models of others’ views of me likewise borrow from my self-views.

Sometimes this back and forth between my self-view and my understanding of how others’ view me can seem counter to self-interest. People behave in ways that confirm others’ expectations of them, even when these expectations are negative (Snyder & Swann, 1978, for a review see Snyder & Stukas, 1999). And people interact with other people in ways such that their self-views are not challenged by others’ views of them and their self-views can double as representations of the others’ views of them, even when this means taking other people as having negative views of them (Swann, 1981).

Self-verification and behavioral confirmation strategies

People use multiple different strategies for achieving a match between their self-views and others’ view of them. These strategies come in at different stages of social interaction.

Prior to and in anticipation of interaction, people seek and more thoroughly engage with information and people with self-views expected to be consistent with their self-views. For example, they spend more time reading statements about themselves that they expect to be consistent with their self-views — even if those particular self-views are negative.

During interaction, people behave in ways that elicit views of them from others that are consistent with their self-views. This is especially true when their self-views are being challenged, say because someone expresses a positive view of an aspect of a person who sees that aspect of themselves negatively. People can “go out of their way” to behave in ways that elicit negative self-views. On the other hand, people can change their self-views and their behavior to match the expectations of others; this primarily happens when a person’s view of a particular aspect of themselves is one they do not regard as certain.

After interaction, people better remember expressions of others’ views of them that are consistent with their own. They also can think about others’ views that were inconsistent in ways that construe them as non-conflicting. On the long term, people gravitate to others’ — including friends and spouses — who view them as they view themselves. Likewise, people seem to push away others who have different views of them.

Do people self-verify in interacting with computers?

Given that people engage in this array of self-verification strategies in interactions with other people, we might expect that they would do the same in interacting with computers, including mobile phones, on-screen agents, voices, and services.

One reason to think that people do self-verify in human–computer interaction is that people respond to computers in a myriad of social ways: people reciprocate with computers, take on computers as teammates, treat computer personalities like human personalities, etc. (for a review see Nass & Moon, 2000). So I expect that people use these same strategies when using interactive technologies — including personal computers, mobile phones, robots, cars, online services, etc.

While empirical research should be carried out to test this basic, well-motivated hypothesis, there is further excitement and importance to the broader implications of this idea and its connections to how people understand new technological systems.

When systems models users

Since the 1980s, it has been quite common for system designers to think about the mental models people have of systems — and how these models are shaped by factors both in and out of the designer’s control (Gentner & Stevens, 1983). A familiar goal has been to lead people to a mental model that “matches” a conceptual model developed by the designer and is approximately equivalent to a true system model as far as common inputs and outputs go.

Many interactive systems develop a representation of their users. So in order to have a good mental model of these systems, people must represent how the system views them. This involves many of the same trade-offs considered above.

These considerations point out some potential problems for such systems. Technologists sometimes talk about the ability to provide serendipitous discovery. Quantifying aspects of one’s own life — including social behavior (e.g., Kass, 2007) and health — is a current trend in research, product development, and DIY and self-experimentation. While sometimes this collected data is then analyzed by its subject (e.g., because the subject is a researcher or hacker who just wants to dig into the data), to the extend that this trend will go mainstream, it will require simplification by building and presenting readily understandable models and views of these systems’ users.

The use of self-verification strategies and behavioral confirmation when interacting with computer systems — not only with people — thus presents a challenge to the ability of such systems to find users who are truly open to self-discovery. I think many of these same ideas apply equally to context-aware services on mobile phones and services that models one’s social network (even if they don’t  present that model outright).

Social responses or more general confirmation bias

That people may self-verify with computers as well as people raises a further question about both self-verification theory and social responses to communication technologies theory (aka the “Media Equation”). We may wonder just how general these strategies and responses are: are these strategies and responses distinctively social?

Prior work on self-verification has left open the degree to which self-verification strategies are particular to self-views, rather than general to all relatively important and confident beliefs and attitudes. Likewise, it is unclear to what extent all experiences, rather than just social interaction (including reading statements written or selected by another person), that might challenge or confirm a self-view are subject to these self-verification strategies.

Inspired by Velleman’s description above, we can think that it is just that other’s views of us have an dangerous potential to result in an explosion of the complexity of the world we need to model (”I have a way of interpreting myself, a way that I want you to interpret me, a way that I think you do interpret me, a way that I think you suspect me of wanting you to interpret me, a way that I think you suspect me of thinking you do interpret me, and so on”). Thus, if other systems can prompt this same regress, then the same frugality with our cognitions should lead to self-verification and behavioral confirmation. This is a reminder that treating media like real life, including treating computers like people, is not clearly non-adaptive (contra Reeves & Nass, 1996) or maladaptive (contra Lee, 2004).

References

But I actually want to comment not on their comments, but on this section:

In an industry where the majority of ads go unclicked, even a small boost can make a big difference. One San Francisco advertising company, Rapleaf, carried out a friend-based campaign for a credit-card company that wanted to sell bank products to existing customers. Tailoring offers based on friends’ responses helped lift the average click rate from 0.9% to 2.7%. Although 97.3% of the people surfed past the ads, the click rate still tripled.

Rapleaf, which has harvested data from blogs, online forums, and social networks, says it follows the network behavior of 480 million people. It furnishes friendship data to help customers fine-tune their promotions. Its studies indicate borrowers are a better bet if their friends have higher credit ratings. This might mean a home buyer with a middling credit risk score of 550 should be treated as closer to 600 if most of his or her friends are in that range, says Rapleaf CEO Auren Hoffman.

The idea is that since you are more likely to behave like your friends, their behavior can be used to profile you and tailor some marketing to be more likely to result in compliance.

In the Persuasive Technology Lab at Stanford University, BJ Fogg has long emphasized how powerful and worrying personalization based on this kind of “persuasion profile” can be. Imagine that rather than just personalizing screens based on the books you are expected to like (a familiar idea), Amazon selects the kinds of influence strategies used based on a representation of what strategies work best against you: “Dean is a sucker for limited-time offers”, “Foot-in-the-door works really well against Domenico, especially when he is buying a gift.”

In 2006 two of our students, Fred Leach and Schuyler Kaye, created this goofy video illustrating approximately this concept:

My sense is that this kind of personalization is in wide use at places like Amazon, except that their “units of analysis/personalization” are individual tactics (e.g., Gold Box offers), rather than the social influence strategies that can be implemented in many ways and in combination with each other.

What’s interesting about the Rapleaf work described by BusinessWeek is that this enables persuasion profiling even before a service provider or marketer knows anything about you — except that you were referred by or are otherwise connected to a person. This gives them the ability to estimate your persuasion profile by using your social neighborhood, even if you haven’t disclosed this information about your social network.

While there has been some research on individual differences in responses to influence strategies (including when used by computers), as far as I know there isn’t much work on just how much the responses of friends covary. As a tool for influencers online, it doesn’t matter as much whether this variation explained by friends’ responses is also explained by other variables, as long as those variables aren’t available for the influencers to collect. But for us social scientists, it would be interesting to understand the mechanism by which there is this relationship: is it just that friends are likely to be similar in a bunch of ways and these predict our “persuasion profiles”, or are the processes of relationship creation that directly involve these similarities.

This is an exciting and scary direction, and I want to learn more about it.

Is this etching a self-portrait by Leonardo da Vinci created hundreds of years ago? That’s what I was told by a Californian friend who had “gone native” in Florence. Another matter: is this, in fact, a commonly believed and shared legend, and what other variations are there on it?

I shared the story with some fellow visitors in Florence on a lunch-time return to the piazza. Ed Chi tried to verify the rumor using a Web search, but with no success.  At least in English, there didn’t seem to be much on this in the Web. (See my photo and comments on Flickr.)

I posted the photo on Flickr. I asked questions on LinkedIn and Yahoo! Answers, with no success. I also asked for help from workers on Mechanical Turk. Here’s part of how I asked for help:

There is a portrait etched in stone on the wall of Palazzo Vecchio in Piazza della Signoria in Florence (Firenza), Italy. It is close behind the copy of the David there. I have heard that there is a legend that this is a self-portrait by Leonardo da Vinci. I am looking for any information about this legend, alternate versions of the legend, or information about the real source of the portrait.

What results have been offered seem to suggest that this legend exists — though perhaps it is “actually” (at least as captured online, since perhaps the Leonardo theorists aren’t as active digital content creators) about Michelangelo:

• Palazzo Vecchio in Italian Wikipedia
• Florentine Legends: Fact or Fiction (in Italian)
• Curiosities in Florence

The best way of finding out seemed to actually be my Flickr photo itself, since that’s where Daniel Witting provided the first two links above — however, this was a few months after the photo was first posted to Flickr. Turkers provided a couple useful links also (”Curiosities” above) on a shorter schedule and with a higher price. (I should have also tried uClue — where many former Google Answers researchers now work. This was recommended by Max Harper, who has studied Q&A sites in detail.)

-

Question and answer services along the lines of Yahoo! Answers rose to global (and U.S.) significance only after success in Korea, where Naver Knowledge iN pioneered the use of an online community to power a Q&A site. A major motivation Korea was the limited amount of Korean content online. With Naver’s offering, Korea’s Internet saavy, English population made information newly available in Korean (and did plenty of other interesting work).

This is as significant a motivation for Q&A sites by English-speaking folks in the U.S., but the present case is an exception.

Some of the questions that made this case interesting to me:

• What culturally-shared beliefs get manifest online? During this whole process, I and others wondered whether perhaps this local legend was only shared orally. It seems that it is represented online after all — at least the Michelangelo variant, but it could have been otherwise.
• How does the pair of languages a task requires knowledge of determine the processes, structres, and communities that are optimal for completing the task? For example, it seems quite important whether the target or source language has many more speakers than the other. (One could think about this simplistically in terms of conditional probabilities of skills with language A given skill with language B and vice verse.)