Here’s Natasha Singer at The New York Times on cascades of memes, idioms, and links through online social networks (e.g., Twitter):

If we keep seeing the same links and catchphrases ricocheting around our social networks, it might mean we are being exposed only to what we want to hear, says Damon Centola, an assistant professor of economic sociology at the Massachusetts Institute of Technology.

“You might say to yourself: ‘I am in a group where I am not getting any views other than the ones I agree with. I’m curious to know what else is out there,’” Professor Centola says.

Consider a new hashtag: diversity.

This is how Singer ends this article in which the central example is “icantdateyou” leading Egypt-related idioms as a trending topic on Twitter. The suggestion here, by Centola and Singer, is that people will notice they are getting a biased perspective of how many people agree with them and what topics people care about — and then will take action to get other perspectives.

Why am I skeptical?

First, I doubt that we really realize the extent to which media — and personalized social media in particular — bias their perception of the frequency of beliefs and events. Even though people know that fiction TV programs (e.g., cop shows) don’t aim to represent reality, heavy TV watchers (on average) substantially overestimate the percent of adult men employed in law enforcement.¹ That is, the processes that produce the “friendly world syndrome” function without conscious awareness and, perhaps, even despite it. So people can’t consciously choose to seek out diverse perspectives if they don’t know they are increasingly missing them.

Second, I doubt that people actually want diversity of perspectives all that much. Even if I realize divergent views are missing from my media experience, why would I seek them out? This might be desirable for some people (but not all), and even for those, the desire to encounter people who radically disagree has its limits.

Similar ideas pop up in a NPR All Things Considered segment by Laura Sydell. This short piece (audio, transcript) is part of NPR’s “Cultural Fragmentation” series. The segment begins with the worry that offline bubbles are replicated online and quotes me describing how attempts to filter for personal relevance also heighten the bias towards agreement in personalized media.

But much of the piece has actually focuses on how one person — Kyra Gaunt, a professor and musician — is using Twitter to connect and converse with new and different people. Gaunt describes her experience on Twitter as featuring debate, engagement, and “learning about black people even if you’ve never seen one before”. Sydell’s commentary identifies the public nature of Twitter as an important factor in facilitating experiencing diverse perspectives:

But, even though there is a lot of conversation going on among African Americans on Twitter, Professor Gaunt says it’s very different from the closed nature of Facebook because tweets are public.

I think this is true to some degree: much of the content produced by Facebook users is indeed public, but Facebook does not make it as easily searchable or discoverable (e.g., through trending topics). But more importantly, Facebook and Twitter differ in their affordances for conversation. Facebook ties responses to the original post, which means both that the original poster controls who can reply and that everyone who replies is part of the same conversation. Twitter supports replies through the @reply mechanism, so that anyone can reply but the conversation is fragmented, as repliers and consumers often do not see all replies. So, as I’ve described, even if you follow a few people you disagree with on Twitter, you’ll most likely see replies from the other people you follow, who — more often than not — you agree with.

Gaunt’s experience with Twitter is certainly not typical. She has over 3,300 followers and follows over 2,400, so many of her posts will generate replies from people she doesn’t know well but whose replies will appear in her main feed. And — if she looks beyond her main feed to the @Mentions page — she will see the replies from even those she does not follow herself. On the other hand, her followers will likely only see her posts and replies from others they follow.²

Nonetheless, Gaunt’s case is worth considering further, as does Sydell:

SYDELL: Gaunt says she’s made new friends through Twitter.

GAUNT: I’m meeting strangers. I met with two people I had engaged with through Twitter in the past 10 days who I’d never met in real time, in what we say in IRL, in real life. And I met them, and I felt like this is my tribe.

SYDELL: And Gaunt says they weren’t black. But the key word for some observers is tribe. Although there are people like Gaunt who are using social media to reach out, some observers are concerned that she is the exception to the rule, that most of us will be content to stay within our race, class, ethnicity, family or political party.

So Professor Gaunt is likely making connections with people she would not have otherwise. But — it is at least tempting to conclude from “this is my tribe” — they are not people with radically different beliefs and values, even if they have arrived at those beliefs and values from a membership in a different race or class.

1. Gerbner, G., Gross, L., Morgan, M., & Signorielli, N. (1980). The “Mainstreaming” of America: Violence Profile No. 11. Journal of Communication, 30(3), 10-29.
2. One nice feature in “new Twitter” — the recently refresh of the Twitter user interface — is that clicking on a tweet will show some of the replies to it in the right column. This may offer an easier way for followers to discover diverse replies to the people they follow. But it is also not particularly usable, as it is often difficult to even trace what a reply is a reply to.

Birds of a feather flock together. Once flying together, a flock is also subject to the same causes (e.g., storms, pests, prey). Our friends, family, neighbors, and colleagues are more similar to us for similar reasons (and others). So we should have no illusions that the behaviors, attitudes, outcomes, and beliefs of our social neighborhood are good indicators of those of other populations — like U.S. adults, Internet users, or homo sapiens of the past, present, or future. The apocryphal Pauline Kael quote “How could Nixon win? No one I know voted for him” suggests both the ease and error of this kind of inference. I take it as a given that people’s estimates of larger populations’ behaviors and beliefs are often biased in the direction of the behaviors and beliefs in their social neighborhoods. This is the case with and without “social media” and filtered activity streams — and even mediated communication in general.

That is, even without media, our personal experiences are not “representative” of the American experience, human experience, etc., but we do (and must) rely on it anyway. One simple cognitive tool here is using “ease of retrieval” to estimate how common or likely some event is: we can estimate how common something is based on how easy it is to think of. So if something prompts someone to consider how common a type of event is, they will (on average) estimate the event as more common if it is more easy to think of an example of the event, imagine the event, etc. And our personal experiences provide these examples and determine how easy they are to bring to mind. Both prompts and immediately prior experience can thus affect these frequency judgments via ease of retrieval effects.

Now this is not to say that we should think as ease of retrieval heuristics as biases per se. Large classes and frequent occurrences are often more available to mind than those that are smaller or less frequent. It is just that this is also often not the case, especially when there is great diversity in frequency among physical and social neighborhoods. But certainly we can see some cases where these heuristics fail.

Media are powerful sources of experiences that can make availability and actual frequency diverge, whether by increasing the biases in the direction of projecting our social neighborhoods onto larger population or in other, perhaps unexpected directions. In a classic and controversial line of research in the 1970s and 80s, Gerbner and colleagues argued that increased television-watching produces a “mean world syndrome” such that watching more TV causes people to increasingly overestimate, e.g., the fraction of adult U.S. men employed in law enforcement and the probability of being a victim of violent crime. Their work did not focus on investigating heuristics producing these effects, but others have suggested the availability heuristic (and related ease of retrieval effects) as at work. So even if my social neighborhood has fewer cops or victims of violent crime than the national average, media consumption and the availability heuristic can lead me to overestimate both.

Personalized and filtered activity streams certainly also affect us through some of the same psychological processes, leading to biases in users’ estimates of population-wide frequencies. They can aIso bias inference about our own social neighborhoods. If I try to estimate how likely a Facebook status update by a friend is to receive a comment, this estimate will be affected by the status updates I have seen recently. And if content with comments is more likely to be shown to me in my personalized filtered activity stream (a simple rule for selecting more interesting content, when there is too much for me to consume it all), then it will be easier for me to think of cases in which status updates by my friends do receive comments.

In my previous posts on these ideas, I have mainly focused on effects on beliefs about my social neighborhood and specifically behaviors and outcomes specific to the service providing the activity stream (e.g., receiving comments). But similar effects apply for beliefs about other behaviors, opinions, and outcomes. In particular, filtered activity streams can increase the sense that my social neighborhood (and perhaps the world) agrees with me. Say that content produced by my Facebook friends with comments and interaction from mutual friends is more likely to be shown in my filtered activity streams. Also assume that people are more likely to express their agreement in such a way than substantial disagreement. As long as I am likely to agree with most of my friends, then this simple rule for filtering produces an activity stream with content I agree with more than an unfiltered stream would. Thus, even if I have a substantial minority of friends with whom I disagree on politics, this filtering rule would likely make me see less of their content, since it is less likely to receive (approving) comments from mutual friends.

I’ve been casually calling this larger family of effects this the “friendly world syndrome” induced by filtered activity streams. Like the mean world syndrome of the television cultivation research described above, this picks out a family of unintentional effects of media. Unlike the mean world syndrome, the friendly world syndrome includes such results as overestimating how many friends I have in common with my friends, how much positive and accomplishment-reporting content my friends produce, and (as described) how much I agree with my friends.¹

Even though the filtering rules I’ve described so far are quite simple and appealing, they still are more consistent with versions of activity streams that are filtered by fancy relevance models, which are often quite opaque to users. Facebook News Feed — and “Top News” in particular — is the standard example here. On the other hand, one might think that these arguments do not apply to Twitter, which does not apply any kind of machine learning model estimating relevance to filtering users’ streams. But Twitter actually does implement a filtering rule with important similarities to the “comments from mutual friends” rule described above. Twitter only shows “@replies” to a user on their home page when that user is following both the poster of the reply and the person being replied to.² This rule makes a lot of sense, as a reply is often quite difficult to understand without the original tweet. Thus, I am much more likely to see people I follow replying to people I follow than to others (since the latter replies are encountered only from browsing away from the home page. I think this illustrates how even a straightforward, transparent rule for filtering content can magnify false consensus effects.

One aim in writing this is to clarify that a move from filtering activity streams using opaque machine learning models of relevance to filtering them with simple, transparent, user-configurable rules will likely be insufficient to prevent the friendly world syndrome. This change might have many positive effects and even reduce some of these effects by making people mindful of the filtering.³ But I don’t think these effects are so easily avoided in any media environment that includes sensible personalization for increased relevance and engagement.

1. This might suggest that some of the false consensus effects observed in recent work using data collected about Facebook friends could be endogenous to Facebook. See Goel, S., Mason, W., & Watts, D. J. (2010). Real and perceived attitude agreement in social networks. Journal of Personality and Social Psychology, 99(4), 611-621. doi:10.1037/a0020697
2. Twitter offers the option to see all @replies written by people one is following, but 98% of users use the default option. Some users were unhappy with an earlier temporary removal of this feature. My sense is that the biggest complaint was that removing this feature removed a valuable means for discovering new people to follow.
3. We are investigating this in ongoing experimental research. Also see Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61(2), 195-202. doi:10.1037/0022-3514.61.2.195

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.

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.)

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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.)

Context-aware suggestion techniques such as those used in ZoneTag can increase tagging, but what about users’ motivations for considering tagging in the first place? And how can these motivations for annotation be considered in designing services that involve annotation? In this post, I consider existing work on motivations for tagging, and I use tagging on Facebook as an example of how multiple motivations can combine to increase desired annotation behaviors.

Using photo-elicitation interviews with ZoneTag users who tag, Ames & Naaman (2007) present a two factor taxonomy of motivations for tagging. First, they categorize tagging motivations by function: is the motivating function of the tagging organizational or communicative? Organizational functions include supporting search, presenting photos by event, etc., while communicative functions include when tags provide information about the photos, their content, or are otherwise part of a communication (e.g., telling a joke). Second, they categorize tagging motivations by intended audience (or sociality): are the tags intended for my future self, people known to me (friends, family, coworkers, online contacts), or the general public?

Taxonomy of motivations for tagging from Ames & Naaman

On Flickr the function dimension generally maps onto the distinction between functionality that enables and is prior to arriving at the given photo or photos (organization) and functionality applicable once one is viewing a photo (communication). For example, I can find a photo (by me or someone else) by searching for a person’s name, and then use other tags applied to that photo to jog my memory of what event the photo was taken at.

Some Flickr users subscribe to RSS feeds for public photos tagged with their name, making for a communication function of tagging — particularly tagging of people in media — that is prior to “arriving” at a specific media object. These are generally techie power users, but this can matter for others. Some less techie participants in our studies reported noticing that their friends did this — so they became aware of tagging those friends’ names as a communicative act that would result in the friends finding the tagged photos.

This kind of function of tagging people is executed more generally — and for more than just techie power users — by Facebook. In tagging of photos, videos, and blog posts, tagging a person notifies them they have been tagged, and can add that they have been tagged to their friends’ News Feeds. This function has received a lot of attention from a privacy perspective (and it should). But I think it hints at the promise of making annotation behavior fulfill more of these functions simultaneously. When specifying content can also be used to specify recipients, annotation becomes an important trigger for communication.

—————

See some interesting comments (from Twitter) about tagging on Facebook:

• noticing people tagging to gain eyeballs
• exhorting others not to tag bad photos (and thanks)
• collapsing time by tagging photos from long ago
• tagging by parents

(Also see Facebook’s growing use and testing of autotagging [1, 2].)

References

News Feed is an example of a particular design pattern that has recently become quite common – the activity stream. An activity stream aggregates actions of a set of individuals – such as a person’s egocentric social network – and displays the recent and/or interesting ones.

I’ve previously analysed, in a more fine-grained analysis of a particular (and now changed) interface element for setting one’s Facebook status message, how activity streams bias our beliefs about the frequency of others’ participation on social network services (SNSs). It works like this:

• We use availability to mind as a heuristic for estimating probability and frequency (Kahneman & Tversky, 1973). So if it is easier to think of a possibility, we judge it to be more likely or frequent. This heuristic is often helpful, but it also leads to bias due to, e.g., recent experience, search strategy (compare thinking of words starting with ‘r’ versus words with ‘r’ as the third letter).
• Activity streams show a recent subset of the activity available (think for now of a simple activity stream, like that on one’s Twitter home page).
• Activity streams show activity that is more likely to be interesting and is more likely to have comments on it.

Through the availability heuristic (and other mechanisms) this leads to one to estimate that (1) people in one’s egocentric network are generating activity on Facebook more frequently than they actually are and (2) stories with particular characteristics (e.g., comments on them) are more (or less) common in one’s egocentric network than they actually are.

Personalized cultivation

When thinking about this in the larger picture, one can see this as a kind of cultivation effect of algorithmic selection processes in interpersonal media. According to cultivation theory (see Williams, 2006, for an application to MMORGs), our long-term exposure to media makes leads us to see the real world through the lens of the media world; this exposure gradually results in beliefs about the world based on the systematic distortions of the media world (Gerbner et al., 1980). For example, heavy television viewing predicts giving more “television world” answers to questions — overestimating the frequency of men working in law enforcement and the probability of experiencing violent acts. A critical difference here is that with activity streams, similar cultivation can occur with regard to our local social and cultural neighborhood.

Aims of personalization

Automated personalization has traditionally focused on optimizing for relevance – keep users looking, get them clicking for more information, and make them participate related to this relevant content. But the considerations here highlight another goal of personalization: personalization for strategic influence on attitudes that matter for participation. These goals can be in tension. For example, should the system present…

The most interesting and relevant photos to a user?

Showing photographs from a user’s network that have many views and comments may result in showing photos that are very interesting to the user. However, seeing these photos can lead to inaccurate beliefs about how common different kinds of photos are (for example, overestimating the frequency of high-quality, artistic photos and underestimating the frequency of “poor-quality” cameraphone photos). This can discourage participation through perceptions of the norms for the network or the community.

On the other hand, seeing photos with so many comments or views may lead to overestimating how many comments one is likely to get on one’s own photo; this can result in disappointment following participation.

Activity from a user’s closest friends?

Assume that activity from close friends is more likely to be relevant and interesting. It might even be more likely to prompt participation, particularly in the form of comments and replies. But it can also bias judgments of likely audience: all those people I don’t know so well are harder to bring to mind as is, but if they don’t appear much in the activity stream for my network, I’m less likely to consider them when creating my content. This could lead to greater self-disclosure, bad privacy experiences, poor identity management, and eventual reduction in participation.

References

Gerbner, G., Gross, L., Morgan, M., & Signorielli, N. (1980). The “Mainstreaming” of America: Violence Profile No. 11. Journal of Communication, 30(3), 10-29.

Kahneman, D., & Tversky, A. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207-232.

Williams, D. (2006). Virtual Cultivation: Online Worlds, Offline Perceptions. Journal of Communication, 56, 69-87.

First, I want to consider a larger category of strategic mediation in which no communication behavior is changed or added between different existing participants. This includes applying influence strategies to the feedback to the communicator as in coaching (e.g., Kass 2007) and modification of the communicator’s identity as presented to himself (i.e. the transformations of the Proteus effect). This extension entails a kind of unification of TSI with persuasive technology for computer-mediated communication (CMC; Fogg 2002, Oinas-Kukkonen & Harjumaa 2008).

Second, I want to consider a larger category of media in which the same general ideas of TSI can be manifest, albeit in quite different ways. As described by Bailenson et al. (2005), TSI is (at least in exemplars) limited to transformations of representations of the kind of non-verbal behavior, sensing, and identity cues that appear in face-to-face communication, and thus in CVEs. I consider examples from other forms of communication, including active mediation of the content, verbal or non-verbal, of a communication.

Feedback and influence strategies: TSI and persuasive technology

TSI is exemplified by direct transformation that is continuous and dynamic, rather than, e.g., static anonymization or pseudonymization. These transformations are complex means to strategic ends, and they function through a “two-step” programmatic-psychological process. For example, a non-verbal behavior is changed (modified, filtered, replaced), and then the resulting representation affects the end through a psychological process in other participants. Similar ends can be achieved by similar means in the second (psychological) step, without the same kind of direct programmatic change of the represented behavior.
In particular, consider coaching of non-verbal behavior in a CVE, a case already considered as an example of TSI (Bailenson et al. 2005, pp. 434-6), if not a particularly central one. In one case, auxiliary information is used to help someone interact more successfully:

In those interactions, we render the interactants’ names over their heads on floating billboards for the experimenter to read. In this manner the experimenter can refer to people by name more easily. There are many other ways to use these floating billboards to assist interactants, for example, reminders about the interactant’s preferences or personality (e.g., “doesn’t respond well to prolonged mutual gaze”). (Bailenson et al. 2005, pp. 435-436)

While this method can bring about change in non-verbal behaviors as represented in the CVE and thus achieve the same strategic goals by the same means in the second (psychological) step, it does not do so in the characteristic TSI way: it doesn’t decouple the representation from the behavior; instead it changes the behavior itself in the desired way. I think our understanding of the core of TSI is improved by excluding this kind of active mediation (even that presented by Bailenson et al.) and considering it instead a proper part of the superset – actively mediated communication. With this broadened scope we can take advantage of the wider range of strategies, taxonomies, and examples available from the study of persuasive technology.

TSI ideas outside CVEs

TSI is established as applying to CVEs. Standard TSI examples take place in CVEs and the feasibility of TSI is discussed with regard to CVEs. This focus is also manifest in the fact that it is behaviors, identity cues, and sensing that are normally available that are the starting point for transformation. Some of the more radical transformations of sensing and identity are nonetheless explained with reference to real-world manifestation: for example, helpers walk like ghosts amongst those you are persuading, reporting back on what they learn.

But I think this latter focus is just an artifact of the fact that, in a CVE, all the strategic transformations have to be manifest as representations of face-to-face encounters. As evidence for the anticipation of the generalization of TSI ideas beyond CVEs, we see that Bailenson et al. (2005, p. 428) introduce TSI with examples from the kind of outright blocking of any representation of particular non-verbal behaviors in telephone calls. Of course, this is not the kind of dynamic transformation characteristic of TSI, but this highlights how TSI ideas make sense outside of CVEs as well. To make it more clear what I mean by this, I present three examples: transformation of a shared drawing, coaching and augmentation in face-to-face conversation, and aggregation and synthesis in an SNS-based event application, like Facebook Events.

This more general notion of actively mediated communication is present in the literature as early as 1968 with the work of Licklider & Taylor (1968). In one interesting example, which is also a great example of 1960s gender roles, a man is draws an arrow-pierced heart with his initials and the initials of a romantic interest or partner, but when this heart is shared with her (perhaps in real time as he draws it), it is rendered as a beautiful heart with little resemblance to his original, poor sketch. The figure illustrating the example is captioned, “A communication system should make a positive contribution to the discovery and arousal of interests” (Licklider & Taylor 1968, p. 26). This example clearly exemplifies the idea of TSI – decoupling the original behavior from its representation in a strategic way that requires an intelligent process (or human-in-the-loop) making the transformation responsive to the specific circumstances and goals.

Licklider & Taylor also consider examples in which computers take an active role in a face-to-face presentation by adding a shared, persuasive simulation (cf. Fogg 2002 on computers in the functional role of interactive media such as games and simulations). But a clearer example, that also bears more resemblance to characteristic TSI examples, is conversation and interaction coaching via a wireless headset that can determine how much each participant is speaking, for how long, and how often they interrupt each other (Kass 2007). One could even imagine a case with greater similarity to the TSI example considered in the previous case: a device whispers in your ear the known preferences of the person you are talking to face-to-face (e.g., that he doesn’t respond well to prolonged mutual gaze).

Strategic transformation of a drawing for a romantic interest (Licklider & Taylor 1968, p. 26).

Finally, I want to share an example that is a bit farther afield from TSI exemplars, but highlights how ubiquitous this general category is becoming. Facebook includes a social event planning application with which users can create and comment on events, state their plans to attend, and share personal media and information before and after it occurs. Facebook presents relevant information about one’s network in a single “News Feed”. Event related items can appear in this feed, and they feature active mediation: a user can see an item stating that “Jeff, Angela, Rich, and 6 other friends are attending X. It is at 9pm tonight” – but none of these people or the event creator, ever wrote this text. It has been generated strategically: it is encouraging considering coming to the event and it is designed to maximize the user’s sense of relevance of their News Feed. The original content, peripheral behavior, and form of their communications have been aggregated and synthesized into a new communication that better suits the situation than the original.

Source orientation in actively mediated communication

Bailenson et al. (2005) considers the consequences of TSI for trust in CVEs and how possible TSI detection is. I’ve suggested that we can see TSI-like phenomena, both actual and possible, outside of CVEs and outside of a narrow version of TSI in which directly changing (programmatically) the represented behavior without changing the actual behavior is required. Many of the same consequences for trust may apply.
But even when the active mediation is to some degree explicit – participants are aware that some active mediation is going on, though perhaps not exactly what – interesting questions about source orientation still apply. There is substantial evidence that people orient to the proximal rather than distal source in use of computers and other media (Sundar & Nass 2000, Nass & Moon 2000), but this work has been limited to relatively simple situations, rather than the complex multi-sourced, actively mediated communications under discussion. I think we should expect that proximality will not consistently predict degree of source orientation (impact of source characteristics) in these circumstances: the most proximal source may be a dumb terminal/pipe (cf. the poor evidence for proximal source orientation in the case of televisions, Reeves & Nass 1996), or the most proximal source may be an avatar, the second most proximal might be a cyranoid/ractor or a computer process, while the more distant is the person whose visual likeness is similar to that of the avatar; and in these cases one would expect the source orientation to not be the most proximal, but to be the sources that are more phenomenologically present and more available to mind.

This seems like a promising direction for research to me. Most generally, it is part of the study of source orientation in more complex configurations – with multiple devices, multiple sources, and multiple brands and identities. Consider a basic three condition experiment in which participants interact with another person and are either told (1) nothing about any active mediation, (2) there is a computer actively mediating the communications of the other person, (3) there is a human (or perhaps multiple humans) actively mediating the communications of the other person. I am not sure this is the best design, but I think it hints in the direction of the following questions:

• When and how do people apply familiar social cognition strategies (e.g., folk psychology of propositional attitudes) to understanding, explaining, and predicting the behavior of a collection of people (e.g., multiple cyranoids, or workers in a task completion market like Amazon Mechanical Turk)?
• What differences are there in social responses, source orientation, and trust between active mediation that is (ostensibly) carried out by (1) a single human, (2) multiple humans each doing very small pieces, (3) a computer?

References

1. This idea is expanded upon in Eckles, Ballagas, and Takayama (ms.), to be presented at the workshop on Socially Mediating Technologies at CHI 2009. This working paper will be available online soon.

A big theme of the workshop this morning has been transitions among production and consumption — and the critical role of annotations and context-awareness in enabling many of the user experiences discussed. In many ways, this workshop took me back to thinking about mobile media sharing, which was at the center of a good deal of my previous work. At Yahoo! Research Berkeley we were informed by Marc Davis’s vision of enabling “the billions of daily media consumers to become daily media producers.” With ZoneTag we used context-awareness, sociality, and simplicity to influence people to create, annotate, and share photos from their mobile phones (Ahern et al. 2006, 2007).

Enabling and encouraging these behaviors (for all media types) remains a major goal for designers of participatory media; and this was explicit at several points throughout the workshop (e.g., in Teppo Raisanen’s broad presentation on persuasive technology). This morning there was discussion about the technical requirements for consuming, capturing, and sending media. Cases that traditionally seem to strictly structure and separate production and consumption may be (1) in need of revision and increased flexibility or (2) actually already involve production and consumption together through existing tools. Media production to be part of a two-way communication, it must be consumed, whether by peers or the traditional producers.

As an example of the first case, Sarah Lewis (Stanford) highlighted the importance of making distance learning experiences reciprocal, rather than enforcing an asymmetry in what media types can be shared by different participants. In a past distance learning situation focused on the African ecosystem, it was frustrating that video was only shared from the participants at Stanford to participants at African colleges — leaving the latter to respond only via text. A prototype system, Mobltz, she and her colleagues have built is designed to change this, supporting the creation of channels of media from multiple people (which also reminded me of Kyte.tv).

As an example of the second case, Timo Koskinenen (Nokia) presented a trial of mobile media capture tools for professional journalists. In this case the work flow of what is, in the end, a media production practice, involves also consumption in the form of review of one’s own materials and other journalists, as they edit, consider what new media to capture.

Throughout the sessions themselves and conversations with participants during breaks and lunch, having good annotations continued to come up as a requirement for many of the services discussed. While I think our ZoneTag work (and the free suggested tags Web service API it provides) made a good contribution in this area, as has a wide array of other work (e.g., von Ahn & Dabbish 2004, licensed in Google Image Labeler), there is still a lot of progress to make, especially in bringing this work to market and making it something that further services can build on.

References

1. Blogging something at this level of roughness is still new for me…

Over at Captology Notebook, the blog of the Stanford Persuasive Technology Lab, Enrique Allen considers features of Facebook that influence users to update their status. Among other things, he highlights how Facebook lowers barriers to updating by giving users a clear sense of something they can right (“What are you doing right now?”).

I’d like to add another part of the interface for consideration: the box in the left box of the home page that shows your current status update with the most recent updates of your friends.

This visual association of my status and the most recent status updates of my friends seems to do at least a couple things:

Influencing the frequency of updates. In this example, my status was updated a few days ago. On the other hand, the status updates from my friends were each updated under an hour ago. This juxtaposes my stale status with the fresh updates of my peers. This can prompt comparison between their frequency of updates and mine, encouraging me to update.

The choice of the most recent updates by my Facebook friends amplifies this effect. Through automatic application of the availability heuristic, this can make me overestimate how recently my friends have updated their status (and thus the frequency of status updates). For example, the Facebook friend who updated their status three minutes ago might have not updated to three weeks prior. Or many of my Facebook friends may not frequently update their status messages, but I only see (and thus have most available to mind) the most recent. This is social influence through enabling and encouraging biased social comparison with — in a sense — an imagined group of peers modeled on those with the most recent performances of the target behavior (i.e., updating status).

Influencing the content of updates. In his original post, Enrique mentions how Facebook ensures that users have the ability to update their status by giving them a question that they can answer. Similarly, this box also gives users examples from their peers to draw on.

Of course, this can all run up against trouble. If I have few Facebook friends, none of them update their status much, or those who do update their status are not well liked by me, this comparison may fail to achieve increased updates.

Consider this interface in comparison to one that either

• showed recent status updates by your closest Facebook friends, or
• showed recent status updates and the associated average period for updates of your Facebook friends that most frequently update their status.

[Update: While the screenshot above is from the "new version" of Facebook, since I captured it they have apparently removed other people's updates from this box on the home page, as Sasha pointed out in the comments. I'm not sure why they would do this, but here are couple ideas:

• make lower items in this sidebar (right column) more visable on the home page -- including the ad there
• emphasize the filter buttons at the top of the news feed (left column) as the means to seeing status updates.

Given the analysis in the original post, we can consider whether this change is worth it: does this decrease status updates? I wonder if Facebook did a A-B test of this: my money would be on this significantly reducing status updates from the home page, especially from users with friends who do update their status.]