But Frege was surely right when he said, “There is no word or sign in language whose function is simply to assert something.” Frege, as we know, set out to rectify matters by inventing such a sign, the turnstile ⊢’ [sometimes called Frege's 'judgment stroke' or 'assertion sign']. And here Frege was operating on the basis of a sound principle: if there is a conventional feature of language, it can be made manifest in the symbolism. However, before Frege invented the assertion sign he ought to have asked himself why no such sign existed before. Imagine this: the actor is acting a scene in which there is supposed to be a fire. (Albee’s Tiny Alice, for example.) It is his role to imitate as persuasively as he can a man who is trying to warn others of a fire. “Fire!” he screams. And perhaps he adds, at the behest of the author, “I mean it! Look at the smoke!” etc. And now a real fire breaks out, and the actor tries vainly to warn the real audience. “Fire!” he screams, “I mean it! Look at the smoke!” etc. If only he had Frege’s assertion sign.

It should be obvious that the assertion sign would do no good, for the actor would have used it in the first place, when he was only acting. Similar reasoning should convince us that it is no help to say that the stage, or the proscenium arch, creates a conventional setting that negates the convention of assertion. For if that were so, the acting convention could be put into symbols also; and of course no actor or director would use it. The plight of the actor is always with us. There is no known, agreed upon, publically recognizable convention for making assertions. Or, for that matter, giving orders, asking questions, or making promises. These are all things we do, often successfully, and our success depends in part on our having made public our intention to do them. But it was not thanks to a convention that we succeeded.¹

1. Davidson, Donald. (1984). Communication and convention. Synthese 59 (1), 3-17.

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.

Of course, this signal can fail for various reasons. The audience may not understand — may see “the Facebook” as a grammatical error. Or widespread attention to Facebook’s history (say, via a fictionalized movie) may put many people in possession of the ability to use this signal, even though they weren’t early users and are not alumni at the appropriate universities.

Worse still, for some audiences, this usage might seem to put the speaker in a late-adopting category, rather than an early-adopting one! For example, in President G. W. Bush’s visit to Facebook today, he said he is now on “the Facebook”. So to many ears, “the Facebook” does exactly the opposite of the effects described above.

In fact, at least one friend has had just this experience: she used “the Facebook” and got a “are you a luddite?” kind of response. To avoid ambiguity (but also subtlety), “the facebook dot com” is still available.

1. Though it is worth noting that by the time of the domain-name change, many more schools had access to Facebook. But I would guess the likelihood of adoption and attachment to the name is lower. Update: see this more detailed timeline of Facebook university launches.

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.

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.

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.

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.