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.

We are all familiar with ideas said to be ahead of their time, Babbage’s analytical engine and da Vinci’s helicopter are classic examples. We are also familiar with ideas “of their time,” ideas that were “in the air” and thus were often simultaneously discovered such as the telephone, calculus, evolution, and color photography. What is less commented on is the third possibility, ideas that could have been discovered much earlier but which were not, ideas behind their time.

In comparing ideas behind and ahead of their times, it’s worth considering the processes that identify them as such.

In the case of ideas ahead of their time, we rely on records and other evidence of their genesis (e.g., accounts of the use of flamethrowers at sea by the Byzantines ). Later users and re-discoverers of these ideas are then in a position to marvel at their early genesis. In trying to see whether some idea qualifies as ahead of its time, this early genesis, lack or use or underuse, followed by extensive use and development together serve as evidence for “ahead of its time” status.

On the other hand, in identifying ideas behind their time, it seems that we need different sorts of evidence. Taborrok uses the standard of whether their fruits could have been produced a long time earlier (“A lot of the papers in say experimental social psychology published today could have been written a thousand years ago so psychology is behind its time”). We need evidence that people in a previous time had all the intellectual resources to generate and see the use of the idea. Perhaps this makes identifying ideas behind their time harder or more contentious.

P(y | do(x))

Perhaps formal causal inference — and some kind of corresponding new notation, such as Pearl’s do(x) operator — is an idea behind its time.¹ Judea Pearl’s account of the history of structural equation modeling seems to suggest just this: exactly what the early developers of path models (Wright, Haavelmo, Simon) needed was new notation that would have allowed them to distinguish what they were doing (making causal claims with their models) from what others were already doing (making statistical claims).²

In fact, in his recent talk at Stanford, Pearl suggested just this — that if the, say, the equality operator = had been replaced with some kind of assignment operator (say, :=), formal causal inference might have developed much earlier. We might be a lot further along in social science and applied evaluation of interventions if this had happened.

This example raises some questions about the criterion for ideas behind their time that “people in a previous time had all the intellectual resources to generate and see the use of the idea” (above). Pearl is a computer scientist by training and credits this background with his approach to causality as a problem of getting the formal language right — or moving between multiple formal languages. So we may owe this recent development to comfort with creating and evaluating the qualities of formal languages for practical purposes — a comfort found among computer scientists. Of course, e.g., philosophers and logicians also have been long comfortable with generating new formalisms. I think of Frege here.

So I’m not sure whether formal causal inference is an idea behind its time (or, if so, how far behind). But I’m glad we have it now.

1. Of course, there is also Rubin’s formalism of potential outcomes. I have found that this becomes very awkward as the causal models get at all complex.
2. See chapter 5 of Pearl, J. (2009). Causality: Models, Reasoning and Inference. 2nd Ed. Cambridge University Press.

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.”

The chatter in Twitter can accurately predict the box-office revenues of upcoming movies weeks before they are released. In fact, Tweets can predict the performance of films better than market-based predictions, such as Hollywood Stock Exchange, which have been the best predictors to date. (Kevin Kelley)

Here is the paper by Asur and Huberman from HP Labs. Also see a similar use of online discussion forums.

But the obvious question from my previous post is, how much improvement do you get by adding more inputs to the model? That is, how does the combined Hollywood Stock Exchange and Twitter chatter model perform? The authors report adding the number of theaters the movie opens in to both models, but not combining them directly.

One obvious way a model gets more complex is by adding predictors. There has recently been a good deal of attention on using the frequency of search terms to predict important goings-on — like flu trends. Sharad Goel et al. (blog post, paper) temper the excitement a bit by demonstrating that simple models using other, existing public data sets outperform the search data. In some cases (music popularity, in particular), adding the search data to the model improves predictions: the more complex combined model can “explain” some of the variance not handled by the more basic non-search-data models.

This echos one big takeaway from the Netflix Prize competition: committees win. The top competitors were all large teams formed from smaller teams and their models were tuned combinations of several models. That is, the strategy is, take a bunch of complex models and combine them.

One way of doing this is just taking a weighted average of the predictions of several simpler models. This works quite well when your measure of the value of your model is root mean squared error (RMSE), since RMSE is convex.

While often the larger model “explains” more of the variance, what “explains” means here is just that the R-squared is larger: less of the variance is error. More complex models can be difficult to understand, just like the phenomena they model. We will continue to need better tools to understand, visualize, and evaluate our models as their complexity increases. I think the committee metaphor will be an interesting and practical one to apply in the many cases where the best we can do is use a weighted average of several simpler, pretty good models.

It is helpful to start with a contrast between two kinds of cases.

Distributing attention towards a single goal

In the first, there is a single task or goal that involves dividing one’s attention, with the targets of attention somehow related, but of course somewhat independent. Patricia Greenfield used Pac-Man as an example: each of the ghosts must be attended to (in addition to Pac-Man himself), and each is moving independently, but each is related to the same larger goal.

Distributing attention among different goals

In the second kind of case, there are two completely unrelated tasks that divide attention, as in playing a game (e.g., solitaire) while also attending to a speech (e.g., in person, on TV). Anthony Wagner noted that in Greenfield’s listing of the benefits and costs of media multitasking, most of the listed benefits applied to the former case, while the costs she listed applied to the later. So keeping these different senses of multitasking straight is important.

Complications

But the conclusion should not be to think that this is a clear and stable distinction that slices multitasking phenomena in just the right way. Consider one ways of putting this distinction: the primary and secondary task can either be directed at the same goal or directed at different goals (or tasks). Let’s dig into this a bit more.²

Byron Reeves pointed out that sometimes “the IMing is about the game.” So we could distinguish whether the goal of the IMing is the same as the goal of the in-game task(s). But this making this kind of distinction requires identity conditions for goals or tasks that enable this distinction. As Ulrich Mayr commented, goals can be at many different levels, so in order to use goal identity as the criterion, one has to select a level in the hierarchy of goals.

Action identities and multitasking

We can think about this hierarchy of goals as the network of identities for an action that are connected with the “by” relation: one does one thing by doing (several) other things. If these goals are the goals of the person as they represent them, then this is the established approach taken by action identification theory (Vallacher & Wegner, 1987) — and this could be valuable lens for thinking about this. Action identification theory claims that people can report an action identity for what they are doing, and that this identity is the “prepotent identity”. This prepotent identity is generally the highest level identity under which the action is maintainable. This means that the prepotent identity is at least somewhat problematic if used to make this distinction between these two types of multitasking because then the distinction would be dependent on, e.g., how automatic or functionally transparent the behaviors involved are.

For example, if I am driving a car and everything is going well, I may represent the action as “seeing my friend Dave”. I may also represent my simultaneous, coordinating phone call with Dave under this same identity. But if driving becomes more difficult, then my prepotent identity will decrease in level in order to maintain the action. Then these two tasks would not share the prepotent action identity.

Prepotent action identities (i.e. the goal of the behavior as represented by the person in the moment) do not work to make this distinction for all uses. But I think that it actually does help makes some good distinctions about the experience of multitasking, especially if we examine change in action identities over time.

To return to case of media multitasking, consider the headline ticker on 24-hour news television. The headline ticker can be more or less related to what the talking heads are going on about. This could be evaluated as a semantic, topical relationship. But considered as a relationship of goals — and thus action identities — we can see that perhaps sometimes the goals coincide even when the content is quite different. For example, my goal may simply to be “get the latest news”, and I may be able to actually maintain this action — consuming both the headline ticker and the talking heads’ statements — under this high level identity. This is an importantly different case then if I don’t actually maintain the action at the level, but instead must descend to — and switch between — two (or more) lower level identities that are associated the two streams of content.

References

Vallacher, R. R., & Wegner, D. M. (1987). What do people think they’re doing? Action identification and human behavior. Psychological Review, 94(1), 3-15. 

1. The full name is the “Seminar on the impacts of media multitasking on children’s learning and development”.
2. As I was writing this, the topic re-emerged in the workshop discussion. I made some comments, but I think I may not have made myself clear to everyone. Hopefully this post is a bit of an improvement.

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.

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.)

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.