I’ve written previously about how filtered activity streams [edit: i.e. news feeds] can lead to biased views of behaviors in our social neighborhoods. Recent conversations with two people writing popular-press books on related topics have helped me clarify these ideas. Here I reprise previous comments on filtered activity streams, aiming to highlight how they apply even in the case of simple and transparent personalization rules, such as those used by Twitter.
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.
- 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 [↩]
- 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. [↩]
- 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:10.1037/0022-3518.104.22.168 [↩]
The Library of Congress has announced that it will be archiving all Twitter posts (tweets). You can find positive reaction on Twitter. But some have also wondered about privacy concerns. Fred Stutzman, for example, points out how even assuming that only unprotected accounts are being archived this can still be problematic.1 While some people have Twitter usernames that easily identify their owners and many allow themselves to be found based on an email address that is publicly associated with their identity, there are also many that do not. If at a future time, this account becomes associated with their identity for a larger audience than they desire, they can make their whole account viewable only by approved followers2, delete the account, or delete some of the tweets. Of course, this information may remain elsewhere on the Internet for a short or long time. But in contrast, the Library of Congress archive will be much more enduring and likely outside of individual users’ control.3 While I think it is worth examining the strategies that people adopt to cope with inflexible or difficult to use privacy controls in software, I don’t intend to do that here.
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.
- This might not be the case, see Michael Zimmer and this New York Times article. [↩]
- 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. [↩]
- 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.” [↩]
Mobile phones are gateways to our most important and enduring relationships with other people. But, like other communication technologies, the mobile phone is psychologically not only a medium: we also form enduring relationships with devices themselves and their associated software and services (Sundar 2004). While different than relationships with other people, these human–technology relationships are also importantly social relationships. People exhibit a host of automatic, social responses to interactive technologies by applying familiar social rules, categories, and norms that are otherwise used in interacting with people (Reeves and Nass 1996; Nass and Moon 2000).
These human–technology relationships develop and endure over time and through radical changes in the situation. In particular, mobile phones are near-constant companions. They take on roles of both medium for communication with other people and independent interaction partner through dynamic physical, social, and cultural environments and tasks. The global phenomenon of mobile phone use highlights both that relationships with people and technologies are inﬂuenced by variable context and that these devices are, in some ways, a constant in amidst these everyday changes.
Situational variation and attribution
Situational variation is important for how people understand and interact with mobile technology. This variation is an input to the processes by which people disentangle the internal (personal or device) and external (situational) causes of an social entity’s behavior (Fiedler et al. 1999; Forsterling 1992; Kelley 1967), so this situational variation contributes to the traits and states attributed to human and technological entities. Furthermore, situational variation inﬂuences the relationship and interaction in other ways. For example, we have recently carried out an experiment providing evidence that this situational variation itself (rather than the characteristics of the situations) inﬂuences memory, creativity, and self-disclosure to a mobile service; in particular, people disclose more in places they have previously disclosed to the service, than in new places (Sukumaran et al. 2009).
Not only does the situation vary, but mobile technologies are increasingly responsive to the environments they share with their human interactants. A system’s systematic and purposive responsiveness to the environment means means that explaining its behavior is about more than distinguishing internal and external causes: people explain behavior by attributing reasons to the entity, which may trivially either refer to internal or external causes. For example, contrast “Jack bought the house because it was secluded” (external) with “Jack bought the house because he wanted privacy” (internal) (Ross 1977, p. 176). Much research in the social cognition and attribution theory traditions of psychology has failed to address this richness of people’s everyday explanations of other ’s behavior (Malle 2004; McClure 2002), but contemporary, interdisciplinary work is elaborating on theories and methods from philosophy and developmental psychology to this end (e.g., the contributions to Malle et al. 2001).
These two developments — the increasing role of situational variation in human-technology relationships and a new appreciation of the richness of everyday explanations of behavior — are important to consider together in designing new research in human-computer interaction, psychology, and communication. Here are three suggestions about directions to pursue in light of this:
Design systems that provide constancy and support through radical situational changes in both the social and physical environment. For example, we have created a system that uses the voices of participants in an upcoming event as audio primes during transition periods (Sohn et al. 2009). This can help ease the transition from a long corporate meeting to a chat with fellow parents at a child’s soccer game.
Design experimental manipulations and measure based on features of folk psychology — the implicit theory or capabilities by which we attribute, e.g., beliefs, thoughts, and desires (propositional attitudes) to others (Dennett 1987) — identified by philosophers. For example, attributions propositional attitudes (e.g., beliefs) to an entity have the linguistic feature that one cannot substitute different terms that refer to the same object while maintaining the truth or appropriateness of the statement. This opacity in attributions of propositional attitudes is the subject of a large literature (e.g., following Quine 1953), but this has not been used as a lens for much empirical work, except for some developmental psychology (e.g., Apperly and Robinson 2003). Human-computer interaction research should use this opacity (and other underused features of folk psychology) in studies of how people think about systems.
Connect work on mental models of systems (e.g., Kempton 1986; Norman 1988) to theories of social cognition and folk psychology. I think we can expect much larger overlap in the process involved than in the current research literature: people use folk psychology to understand, predict, and explain technological systems — not just other people.
Apperly, I. A., & Robinson, E. J. (2003). When can children handle referential opacity? Evidence for systematic variation in 5- and 6-year-old children’s reasoning about beliefs and belief reports. Journal of Experimental Child Psychology, 85(4), 297-311. doi: 10.1016/S0022-0965(03)00099-7.
Dennett, D. C. (1987). The Intentional Stance (p. 388). MIT Press.
Fiedler, K., Walther, E., & Nickel, S. (1999). Covariation-based attribution: On the ability to assess multiple covariates of an effect. Personality and Social Psychology Bulletin, 25(5), 609.
Försterling, F. (1992). The Kelley model as an analysis of variance analogy: How far can it be taken? Journal of Experimental Social Psychology, 28(5), 475-490. doi: 10.1016/0022-1031(92)90042-I.
Kelley, H. H. (1967). Attribution theory in social psychology. In Nebraska Symposium on Motivation (Vol. 15).
Malle, B. F. (2004). How the Mind Explains Behavior: Folk Explanations, Meaning, and Social Interaction. Bradford Books.
Malle, B. F., Moses, L. J., & Baldwin, D. A. (2001). Intentions and Intentionality: Foundations of Social Cognition. MIT Press.
McClure, J. (2002). Goal-Based Explanations of Actions and Outcomes. In M. H. Wolfgang Stroebe (Ed.), European Review of Social Psychology (pp. 201-235). John Wiley & Sons, Inc. Retrieved from http://dx.doi.org/10.1002/0470013478.ch7.
Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1), 81-103.
Norman, D. A. (1988). The Psychology of Everyday Things. New York: Basic Books.
Quine, W. V. O. (1953). From a Logical Point of View: Nine Logico-Philosophical Essays. Harvard University Press.
Reeves, B., & Nass, C. (1996). The media equation: how people treat computers, television, and new media like real people and places (p. 305). Cambridge University Press.
Ross, L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology (Vol. 10, pp. 174-221). New York: Academic Press.
Sohn, T., Takayama, L., Eckles, D., & Ballagas, R. (2009). Auditory Priming for Upcoming Events. Forthcoming in CHI ’09 extended abstracts on Human factors in computing systems. Boston, Massachusetts, United States: ACM Press.
Sukumaran, A., Ophir, E., Eckles, D., & Nass, C. I. (2009). Variable Environments in Mobile Interaction Aid Creativity but Impair Learning and Self-disclosure. To be presented at the Association for Psychological Science Convention, San Francisco, California.
Sundar, S. S. (2004). Loyalty to computer terminals: is it anthropomorphism or consistency? Behaviour & Information Technology, 23(2), 107-118.
Every person who logs into Facebook is met with the same interface but with personalized content. This interface is News Feed, which lists “news stories” generated by users’ Facebook friend. These news stories include the breaking news that Andrew was just tagged in a photo, that Neema declared he is a fan of a particular corporation, that Ellen joined a group expressing support for a charity, and that Alan says, “currently enjoying an iced coffee… anyone want to see a movie tonight?”
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.
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.
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.
This post revisits some thoughts I’ve shared an earlier version of here. In articles over the past few years, John Bargh and his colleagues claim that cognitive psychology has operated with a narrow definition of unconscious processing that has led investigators to describe it as “dumb” and “limited”. Bargh prefers a definition of unconscious processing more popular in social psychology – a definition that allows him to claim a much broader, more pervasive, and “smarter” role for unconscious processing in our everyday lives. In particular, I summarize the two definitions used in Bargh’s argument (Bargh & Morsella 2008, p. 1) as the following:
Unconscious processingcog is the processing of stimuli of which one is unaware.
Unconscious processingsoc is processing of which one is unaware, whether or not one is aware of the stimuli.
A helpful characterization of unconscious processingsoc is the question: “To what extent are people aware of and able to report on the true causes of their behavior?” (Nisbett & Wilson 1977). We can read this project as addressing first-person authority about causal trees that link external events to observable behavior.
What does it mean for the processing of a stimulus to be below conscious awareness? In particular, we can wonder, what is that one is aware of when one is aware of a mental process of one’s own? While determining whether unconscious processingcog is going on requires specifying a stimulus to which the question is relative, unconscious processingsoc requires specifying a process to which the question is relative. There may well be troubles with specifying the stimulus, but there seem to be bigger questions about specifying the process.
There are many interesting and complex ways to identify a process for consideration or study. Perhaps the simplest kind of variation to consider is just differences of detail. First, consider the difference between knowing some general law about mental processing and knowing that one has in fact engaging in processing meeting the conditions of application for the law.
Second, consider the difference between knowing that one is processing some stimulus and that a various long list of things have a causal role (cf. the generic observation that causal chains are hard to come by, but causal trees are all around us) and knowing the specific causal role each has and the truth of various counterfactuals for situations in which those causes were absent.
Third, consider the difference between knowing that some kind of processing is going on that will accomplish an end (something like knowing the normative functional or teleological specification of the process, cf. Millikan 1990 on rule-following and biology) and the details of the implementation of that process in the brain (do you know the threshold for firing on that neuron?). We can observe that an extensionally identical process can always be considered under different descriptions; and any process that one is aware of can be decomposed into a description of extensionally identical sub-processes, of which one is unaware.
A bit trickier are variations in descriptions of processes that do not have law-like relationships between each other. For example, there are good arguments for why folk psychological descriptions of processes (e.g. I saw that A, so I believed that B, and, because I desired that C, I told him that D) are not reducible to descriptions of processes in physical or biological terms about the person.1
We are still left with the question: What does it mean to be unaware of the imminent consequences of processing a stimulus?
Anscombe, G. (1969). Intention. Oxford: Blackwell Publishers.
Bargh, J. A., & Morsella, E. (2008). The unconscious mind. Perspectives on Psychological Science, 3(1), 73-79.
Davidson, D. (1963). Actions, Reasons, and Causes. Journal of Philosophy, 60(23), 685-700.
Millikan, R. G. (1990). Truth Rules, Hoverflies, and the Kripke-Wittgenstein Paradox. Philosophical Review, 99(3), 323-53.
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231-259.
Putnam, H. (1975). The Meaning of ‘Meaning’. In K. Gunderson (Ed.), Language, Mind and Knowledge. Minneapolis: University of Minnesota Press.
- There are likely more examples of this than commonly thought, but the one I am thinking of is the most famous: the weak supervenience of mental (intentional) states on physical states without there being psychophysical laws linking the two (Davidson 1963, Anscombe 1969, Putnam 1975). [↩]