Applying social psychology

Some reflections on how “quantitative” social psychology is and how this matters for its application to design and decision-making — especially in industries touched by the Internet.

In many ways, contemporary social psychology is dogmatically quantitative. Investigators run experiments, measure quantitative outcomes (even coding free responses to make them amenable to analysis), and use statistics to characterize the collected data. On the other hand, social psychology’s processes of stating and integrating its conclusions remain largely qualitative. Many hypotheses in social psychology state that some factor affects a process or outcome in one direction (i.e., “call” either beta > 0 or beta < 0). Reviews of research in social psychology often start with a simple effect and then note how many other variables moderate this effect. This is all quite fitting with the dominance of null-hypothesis significance testing (NHST) in much of psychology: rather than producing point estimates or confidence intervals for causal effects, it is enough to simply see how likely the observed data is given there there is no effect.1 Of course, there have been many efforts to change this. Many journals require reporting effect sizes. This is a good thing, but these effect sizes are rarely predicted by social psychological theory. Rather, they are reported to aid judgments of whether a finding is not only statistically significant but substantively or practically significant, and the theory predicts the direction of the effect. Not only is this process of reporting and combining results not quantitative in many ways, but it requires substantial inference from the particular settings of conducted experiments to the present settings. This actually helps to make sense of the practices described above: many social psychology experiments are conducted in conditions and with populations that are so different from those in which people would like to apply the resulting theories, that expecting consistency of effect sizes is implausible.2 This is not to say that these studies cannot tell us a good deal about how people will behave in many circumstances. It's just that figuring out what they predict and whether these predictions are reliable is a very messy, qualitative process. Thus, when it comes to making decisions -- about a policy, intervention, or service -- based on social-psychological research, this process is largely qualitative. Decision-makers can ask, which effects are in play? What is their direction? With interventions and measurement that are very likely different from the present case, how large were the effects?3

Sometimes this is the best that social science can provide. And such answers can be quite useful in design. The results of psychology experiments can often be very effective when used generatively. For example, designers can use taxonomies of persuasive strategies to dream up some ways of producing desired behavior change.

Nonetheless, I think all this can be contrasted with some alternative practices that are both more quantitative and require less of this uneasy generalization. First, social scientists can give much more attention to point estimates of parameters. While not without its (other) flaws, the economics literature on financial returns to education has aimed to provide, criticize, and refine estimates of just how much wages increase (on average) with more education.4

Second, researchers can avoid much of the messiest kinds of generalization altogether. Within the Internet industry, product optimization experiments are ubiquitous. Google, Yahoo, Facebook, Microsoft, and many others are running hundreds to thousands of simultaneous experiments with parts of their services. This greatly simplifies generalization: the exact intervention under consideration has just been tried with a random sample from the very population it will be applied to. If someone wants to tweak the intervention, just try it again before launching. This process still involves human judgment about how to react to these results.5 An even more extreme alternative is when machine learning is used to fine-tune, e.g., recommendations without direct involvement (or understanding) by humans.

So am I saying that social psychology — at least as an enterprise that is useful to designers and decision-makers — is going to be replaced by simple “bake-off” experiments and machine learning? Not quite. Unlike product managers at Google, many decision-makers don’t have the ability to cheaply test a proposed intervention on their population of interest.6 Even at Google, many changes (or new products) under consideration are too difficult to build to them all: one has to decide among an overabundance of options before the most directly applicable data could be available. This is consistent with my note above that social-psychological findings can make excellent inspiration during idea generation and early evaluation.

  1. To parrot Andrew Gelman, in social phenomena, everything affects everything else. There are no betas that are exactly zero. []
  2. It's also often implausible that the direction of the effect must be preserved. []
  3. Major figures in social psychology, such as Lee Ross, have worked on trying to better anticipate the effects of social interventions from theory. It isn’t easy. []
  4. The diversity of the manipulations used by social psychologists ostensibly studying the same thing can make this more difficult. []
  5. Generalization is not avoided. In particular, decision-makers often have to consider what would happen if an intervention tested with 1% of the population is launched for the whole population. There are all kinds of issues relating to peer influence, network effects, congestion, etc., here that don’t allow for simple extrapolation from the treatment effects identified by the experiment. Nonetheless, these challenges obviously apply to most research that aims to predict the effects of causes. []
  6. However, Internet services play a more and more central role in many parts of our life, so this doesn’t just have to be limited to the Internet industry itself. []

Will the desire for other perspectives trump the “friendly world syndrome”?

Some recent journalism at NPR and The New York Times has addressed some aspects of the “friendly world syndrome” created by personalized media. A theme common to both pieces is that people want to encounter different perspectives and will use available resources to do so. I’m a bit more skeptical.

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.2 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.3

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. I was also interviewed for the NPR segment. []
  3. 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. []

The “friendly world syndrome” induced by simple filtering rules

I’ve written previously about how filtered activity streams 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.

  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 []

Search queries in referrer headers: Technical knowledge, privacy, and the status quo

I have been fascinated by Christopher Soghoian‘s complaint to the FTC about Google’s practices of including search query information in the HTTP referrer header.

In summary, Google has taken proactive efforts to ensure that Web site owners that get visitors from Google search receive the search terms entered by Google’s users. Meanwhile, Google has agreed that search query data is personally sensitive information and that it does not disclosure this information, except under specific, limited circumstances; this is reflected in its privacy policy. Note that Google has not just let the URL do the work, but has specifically worked to make the referrer header include search terms (and additional information) when it has adopted techniques that would otherwise prevent these disclosures from being made. (For a fuller summary, see his blog post and this WSJ article. Or this article at Search Engine Land.)

I am not going to discuss the ethics and legal issues in this particular case. Instead, I just want to draw attention to how this issue reveals the importance of technical knowledge in thinking about privacy issues.

A common response from people working in the Internet industry is that Soghoian is a non-techie that has suddenly “discovered” referrer headers. For example, Danny Sullivan writes “former FTC employee discovers browsers sends referrer strings, turns it into google conspiracy”. (Of course, Soghoian is actually technically savvy, as reading the complaint to the FTC makes clear.)

What’s going on here? Folks with technical knowledge perceive search query disclosure as the status quo (though I bet most don’t often think about the consequences of clicking on a link after a sensitive search).

But how would most Internet users be aware of this? Certainly not through Google’s statements, or through warnings from Web browsers. One of the few ways I think users might realize this is happening is through query-highlighting — on forums, mailing list archives, and spammy pages. So a super-rational user who cares to think about how that works, might guess something like this is going on. But I doubt most users would actively work out the mechanisms involved. Futhermore, their observations likely radically underdetermine the mechanism anyway, since it is quite reasonable that a Web browser could do this kind of highlighting directly, especially for formulaic sites, like forums. Even casual use of Web analytics software (such as Google Analytics) may not make it clear that this per-user information is being provided, since aggregated data could reasonably be used to present summaries of top search queries leading to a Web site.1

This should be a reminder why empirical studies of privacy attitudes and behaviors are useful: us techie folks often have severe blind spots. I don’t know that this is just a matter of differences in expectations, but rather involves differences in preferences. Over time, these expectations change our sense of the status quo, from which we can calibrate our preferences and intentions.

Google has worked to ensure that referrer headers continue to include search query information — even as it adopts techniques that would make this not happen simply by the standard inclusion of the URL there.2 A difference in beliefs about the status quo puts these actions by Google in a different context. For us techies, that is just maintaining the status quo (which may seem more desirable, since we know it’s the industry-wide standard). For others, it might seem more like Google putting advertisers and Web site owners above its promises to its users about their sensitive data.

  1. Google does separately provide aggregated query data to Web site owners. []
  2. See Danny Sullivan’s post following some changes by Google that could have ended including search queries in referrer headers. []

Economic imperialism and causal inference

And I, for one, welcome our new economist overlords…

Readers not in academic social science may take the title of this post as indicating I’m writing about the use of economic might to imperialist ends.1 Rather, economic imperialism is a practice of economists (and acolytes) in which they invade research territories that traditionally “belong” to other social scientific disciplines.2 See this comic for one way you can react to this.3

Economists bring their theoretical, statistical, and research-funding resources to bear on problems that might not be considered economics. For example, freakonomists like Levitt study sumo wrestlers and the effects of the legalization of abortion on crime. But, hey, if the Commerce Clause means that Congress can legislate everything, then, for the same reasons, economists can — no, must — study everything.

I am not an economist by training, but I have recently had reason to read quite a bit in econometrics. Overall, I’m impressed.4 Economists have recently taken causal inference — learning about cause and effect relationships, often from observational data — quite seriously. In the eyes of some, this has precipitated a “credibility revolution” in economics. Certainly, papers in economics and (especially) econometrics journals consider threats to the validity of causal inference at length.

On the other hand, causal inference in the rest of the social sciences is simultaneously over-inhibited and under-inhibited. As Judea Pearl observes in his book Causality, lack of clarity about statistical models (that social scientists often don’t understand) and causality has induced confusion about distinctions between statistical and causal issues (i.e., between estimation methods and identification).5

So, on the one had, many psychologists stick to experiments. Randomized experiments are, generally, the gold standard for investigating cause–effect relationships, so this can and often does go well. However, social psychologists have recently been obsessed with using “mediation analysis” to investigate the mechanisms by which causes they can manipulate produce effects of interest. Investigators often manipulate some factors experimentally and then measure one or more variables they believe fully or partially mediate the effect of those factors on their outcome. Then, under the standard Baron & Kenny approach, psychologists fit a few regression models, including regressing the outcome on both the experimentally manipulated variables and the simply measured (mediating) variables. The assumptions required for this analysis to identify any effects of interest are rarely satisfied (e.g., effects on individuals are homogenous).6 So psychologists are often over-inhibited (experiments only please!) and under-inhibited (mediation analysis).

Likewise, in more observational studies (in psychology, sociology, education, etc.), investigators are sometimes wary of making explicit causal claims. So instead of carefully stating the causal assumptions that would justify different causal conclusions, readers are left with phrases like “suggests” and “is consistent with” followed by causal claims. Authors then recommend that further research be conducted to better support these causal conclusions. With these kinds of recommendations awaiting, no wonder that economists find the territory ready for taking: they can just show up with econometrics tools and get to work on hard-won questions that “rightly belong to others”.

  1. Well, if economists have better funding sources, this might apply in some sense. []
  2. For arguments in favor of economic imperialism, see Lazear, E.P. (1999). Economic imperialism. NBER Working Paper No. 7300. []
  3. Or see this comic for imperialism by physicists. []
  4. At least by the contemporary literature on what I’ve been reading on — IVs, encouragement designs, endogenous interactions, matching estimators. But it is true that in some of these areas econometrics has been able to fruitfully borrow from work on potential outcomes in statistics and epidemiology. []
  5. Econometricians have made similar observations. []
  6. For a bit on this topic, see the discussion and links to papers here. []