Ready-to-hand Dean Eckles on people, technology & inference

How are psychographic personalization and persuasion profiling different from more familiar forms of personalization and recommendation systems? A big difference is that they focus on selecting the “how” or the “means” of inducing you to an action — rather than selecting the “what” or the “ends”. Given the recent interest in this kind of personalization, I wanted to highlight some excerpts from something Maurits Kaptein and I wrote in 2010.¹

This post excerpts our 2010 article, a version of which was published as:
Kaptein, M., & Eckles, D. (2010). Selecting effective means to any end: Futures and ethics of persuasion profiling. In International Conference on Persuasive Technology (pp. 82-93). Springer Lecture Notes in Computer Science.

For more on this topic, see these papers.

We distinguish between those adaptive persuasive technologies that adapt the particular ends they try to bring about and those that adapt their means to some end.

First, there are systems that use models of individual users to select particular ends that are instantiations of more general target behaviors. If the more general target behavior is book buying, then such a system may select which specific books to present.

Second, adaptive persuasive technologies that change their means adapt the persuasive strategy that is used — independent of the end goal. One could offer the same book and for some people show the message that the book is recommended by experts, while for others emphasizing that the book is almost out of stock. Both messages be may true, but the effect of each differs between users.

Example 2. Ends adaptation in recommender systems

Pandora is a popular music service that tries to engage music listeners and persuade them into spending more time on the site and, ultimately, subscribe. For both goals it is beneficial for Pandora if users enjoy the music that is presented to them by achieving a match between the music offering to individual, potentially latent music preferences. In doing so, Pandora adaptively selects the end — the actual song that is listened to and that could be purchased, rather than the means — the reasons presented for the selection of one specific song.

The distinction between end-adaptive persuasive technologies and means-adaptive persuasive technologies is important to discuss since adaptation in the latter case could be domain independent. In end adaptation, we can expect that little of the knowledge of the user that is gained by the system can be used in other domains (e.g. book preferences are likely minimally related to optimally specifying goals in a mobile exercise coach). Means adaptation is potentially quite the opposite. If an agent expects that a person is more responsive to authority claims than to other influence strategies in one domain, it may well be that authority claims are also more effective for that user than other strategies in a different domain. While we focus on novel means-adaptive systems, it is actually quite common for human influence agents adaptively select their means.

Influence Strategies and Implementations

Means-adaptive systems select different means by which to bring about some attitude or behavior change. The distinction between adapting means and ends is an abstract and heuristic one, so it will be helpful to describe one particular way to think about means in persuasive technologies. One way to individuate means of attitude and behavior change is to identify distinct influence strategies, each of which can have many implementations. Investigators studying persuasion and compliance-gaining have varied in how they individuate influence strategies: Cialdini [5] elaborates on six strategies at length, Fogg [8] describes 40 strategies under a more general definition of persuasion, and others have listed over 100 [16].

Despite this variation in their individuation, influence strategies are a useful level of analysis that helps to group and distinguish specific influence tactics. In the context of means adaptation, human and computer persuaders can select influence strategies they expect to be more effective that other influence strategies. In particular, the effectiveness of a strategy can vary with attitude and behavior change goals. Different influence strategies are most effective in different stages of the attitude to behavior continuum [1]. These range from use of heuristics in the attitude stage to use of conditioning when a behavioral change has been established and needs to be maintained [11]. Fogg [10] further illustrates this complexity and the importance of considering variation in target behaviors by presenting a two-dimensional matrix of 35 classes behavior change that vary by (1) the schedule of change (e.g., one time, on cue) and (2) the type of change (e.g., perform new behavior vs. familiar behavior). So even for persuasive technologies that do not adapt to individuals, selecting an influence strategy — the means — is important. We additionally contend that influence strategies are also a useful way to represent individual differences [9] — differences which may be large enough that strategies that are effective on average have negative effects for some people.

Example 4. Backfiring of influence strategies

John just subscribed to a digital workout coaching service. This system measures his activity using an accelerometer and provides John feedback through a Web site. This feedback is accompanied by recommendations from a general practitioner to modify his workout regime. John has all through his life been known as authority averse and dislikes the top-down recommendation style used. After three weeks using the service, John’s exercise levels have decreased.

Persuasion Profiles

When systems represent individual differences as variation in responses to influence strategies — and adapt to these differences, they are engaging in persuasion profiling. Persuasion profiles are thus collections of expected effects of different influence strategies for a specific individual. Hence, an individual’s persuasion profile indicates which influence strategies — one way of individuating means of attitude and behavior change — are expected to be most effective.

Persuasion profiles can be based on demographic, personality, and behavioral data. Relying primarily on behavioral data has recently become a realistic option for interactive technologies, since vast amounts of data about individuals’ behavior in response to attempts at persuasion are currently collected. These data describe how people have responded to presentations of certain products (e.g. e-commerce) or have complied to requests by persuasive technologies (e.g. the DirectLife Activity Monitor [12]).

Existing systems record responses to particular messages — implementations of one or more influence strategies — to aid profiling. For example, Rapleaf uses responses by a users’ friends to particular advertisements to select the message to present to that user [2]. If influence attempts are identified as being implementations of particular strategies, then such systems can “borrow strength” in predicting responses to other implementations of the same strategy or related strategies. Many of these scenarios also involve the collection of personally identifiable information, so persuasion profiles can be associated with individuals across different sessions and services.

Consequences of Means Adaptation

In the remainder of this paper we will focus on the implications of the usage of persuasion profiles in means-adaptive persuasive systems. There are two properties of these systems which make this discussion important:

1. End-independence: Contrary to profiles used by end-adaptive persuasive sys- tems the knowledge gained about people in means-adaptive systems can be used independent from the end goal. Hence, persuasion profiles can be used independent of context and can be exchanged between systems.

2. Undisclosed: While the adaptation in end-adaptive persuasive systems is often most effective when disclosed to the user, this is not necessarily the case in means-adaptive persuasive systems powered by persuasion profiles. Selecting a different influence strategy is likely less salient than changing a target behavior and thus will often not be noticed by users.

Although through the previous examples and the discussion of adaptive persuasive systems these two notions have already been hinted upon, we feel it is important to examine each in more detail.

End-Independence

Means-adaptive persuasive technologies are distinctive in their end-independence: a persuasion profile created in one context can be applied to bringing about other ends in that same context or to behavior or attitude change in a quite different context. This feature of persuasion profiling is best illustrated by contrast with end adaptation.

Any adaptation that selects the particular end (or goal) of a persuasive attempt is inherently context-specific. Though there may be associations between individual differences across context (e.g., between book preferences and political attitudes) these associations are themselves specific to pairs of contexts. On the other hand, persuasion profiles are designed and expected to be independent of particular ends and contexts. For example, we propose that a person’s tendency to comply more to appeals by experts than to those by friends is present both when looking at compliance to a medical regime as well as purchase decisions.

It is important to clarify exactly what is required for end-independence to obtain. If we say that a persuasion profile is end-independent than this does not imply that the effectiveness of influence strategies is constant across all contexts. Consistent with the results reviewed in section 3, we acknowledge that influence strategy effectiveness depends on, e.g., the type of behavior change. That is, we expect that the most effective influence strategy for a system to employ, even given the user’s persuasion profile, would depend on both context and target behavior. Instead, end-independence requires that the difference between the average effect of a strategy for the population and the effect of that strategy for a specific individual is relatively consistent across contexts and ends.

Implications of end-independence.

From end-independence, it follows that persuasion profiles could potentially be created by, and shared with, a number of systems that use and modify these profiles. For example, the profile constructed from observing a user’s online shopping behavior can be of use in increasing compliance in saving energy. Behavioral measures in latter two contexts can contribute to refining the existing profile.²

Not only could persuasion profiles be used across contexts within a single organization, but there is the option of exchanging the persuasion profiles between corporations, governments, other institutions, and individuals. A market for persuasion profiles could develop [9], as currently exists for other data about consumers. Even if a system that implements persuasion profiling does so ethically, once constructed the profiles can be used for ends not anticipated by its designers.

Persuasion profiles are another kind of information about individuals collected by corporations that individuals may or have effective access to. This raises issues of data ownership. Do individuals have access to their complete persuasion profiles or other indicators of the contents of the profiles? Are individuals compensated for this valuable information [14]? If an individual wants to use Amazon’s persuasion profile to jump-start a mobile exercise coach’s adaptation, there may or may not be technical and/or legal mechanisms to obtain and transfer this profile.

Non-disclosure

Means-adaptive persuasive systems are able and likely to not disclose that they are adapting to individuals. This can be contrasted with end adaptation, in which it is often advantageous for the agent to disclose the adaption and potentially easy to detect. For example, when Amazon recommends books for an individual it makes clear that these are personalized recommendations — thus benefiting from effects of apparent personalization and enabling presenting reasons why these books were recommended. In contrast, with means adaptation, not only may the results of the adaptation be less visible to users (e.g. emphasizing either “Pulitzer Prize winning” or “International bestseller”), but disclosure of the adaptation may reduce the target attitude or behavior change.

It is hypothesized that the effectiveness of social influence strategies is, at least partly, caused by automatic processes. According to dual-process models [4], un- der low elaboration message variables manipulated in the selection of influence strategies lead to compliance without much thought. These dual-process models distinguish between central (or systematic) processing, which is characterized by elaboration on and consideration of the merits of presented arguments, and pe- ripheral (or heuristic) processing, which is characterized by responses to cues as- sociated with, but peripheral to the central arguments of, the advocacy through the application of simple, cognitively “cheap”, but fallible rules [13]. Disclosure of means adaptation may increase elaboration on the implementations of the selected influence strategies, decreasing their effectiveness if they operate primarily via heuristic processing. More generally, disclosure of means adaptation is a disclosure of persuasive intent, which can increase elaboration and resistance to persuasion.

Implications of non-disclosure. The fact that persuasion profiles can be obtained and used without disclosing this to users is potentially a cause for concern. Potential reductions in effectiveness upon disclosure incentivize system designs to avoid disclosure of means adaptation.

Non-disclosure of means adaptation may have additional implications when combined with value being placed on the construction of an accurate persuasion profile. This requires some explanation. A simple system engaged in persuasion profiling could select influence strategies and implementations based on which is estimated to have the largest effect in the present case; the model would thus be engaged in passive learning. However, we anticipate that systems will take a more complex approach, employing active learning techniques [e.g., 6]. In active learning the actions selected by the system (e.g., the selection of the influence strategy and its implementation) are chosen not only based on the value of any resulting attitude or behavior change but including the value predicted improvements to the model resulting from observing the individual’s response. Increased precision, generality, or comprehensiveness of a persuasion profile may be valued (a) because the profile will be more effective in the present context or (b) because a more precise profile would be more effective in another context or more valuable in a market for persuasion profiles.

These later cases involve systems taking actions that are estimated to be non-optimal for their apparent goals. For example, a mobile exercise coach could present a message that is not estimated to be the most effective in increasing overall activity level in order to build a more precise, general, or comprehensive persuasion profile. Users of such a system might reasonably expect that it is designed to be effective in coaching them, but it is in fact also selecting actions for other reasons, e.g., selling precise, general, and comprehensive persuasion profiles is part of the company’s business plan. That is, if a system is designed to value constructing a persuasion profile, its behavior may differ substantially from its anticipated core behavior.

1. We were of course influenced by B.J. Fogg’s previous use of the term ‘persuasion profiling’, including in his comments to the Federal Trade Commission in 2006. [↩]
2. This point can also be made in the language of interaction effects in analysis of variance: Persuasion profiles are estimates of person–strategy interaction effects. Thus, the end-independence of persuasion profiles requires not that the two-way strategy– context interaction effect is small, but that the three-way person–strategy–context interaction is small. [↩]

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. [↩]

When Facebook was sweeping Stanford in Spring 2004, it wasn’t yet just Facebook — it was [thefacebook.com]. Many of my friends who were undergrads at Stanford around that time (and shortly after) will still refer to it as “The Facebook” or “the facebook dot com”. This usage can be a jokey signal to members of the in-group that one was an early user. This also may signal attendance at one of the universities Facebook was available at early on (e.g., Harvard, Stanford, Yale, Columbia).¹

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. [↩]

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.

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.” [↩]

BusinessWeek has an exhuberant review of current industry research and product development related to understanding social networks using data from social network sites and other online communication such as email. It includes snippets from people doing very interesting social science research, like Duncan Watts, Cameron Marlow, and danah boyd. So it is worth checking out, even if you’re already familiar with the Facebook Data Team’s recent public reports (“Maintained Relationships”, “Gesundheit!”).

But I actually want to comment not on their comments, but on this section:

In an industry where the majority of ads go unclicked, even a small boost can make a big difference. One San Francisco advertising company, Rapleaf, carried out a friend-based campaign for a credit-card company that wanted to sell bank products to existing customers. Tailoring offers based on friends’ responses helped lift the average click rate from 0.9% to 2.7%. Although 97.3% of the people surfed past the ads, the click rate still tripled.

Rapleaf, which has harvested data from blogs, online forums, and social networks, says it follows the network behavior of 480 million people. It furnishes friendship data to help customers fine-tune their promotions. Its studies indicate borrowers are a better bet if their friends have higher credit ratings. This might mean a home buyer with a middling credit risk score of 550 should be treated as closer to 600 if most of his or her friends are in that range, says Rapleaf CEO Auren Hoffman.

The idea is that since you are more likely to behave like your friends, their behavior can be used to profile you and tailor some marketing to be more likely to result in compliance.

In the Persuasive Technology Lab at Stanford University, BJ Fogg has long emphasized how powerful and worrying personalization based on this kind of “persuasion profile” can be. Imagine that rather than just personalizing screens based on the books you are expected to like (a familiar idea), Amazon selects the kinds of influence strategies used based on a representation of what strategies work best against you: “Dean is a sucker for limited-time offers”, “Foot-in-the-door works really well against Domenico, especially when he is buying a gift.”

In 2006 two of our students, Fred Leach and Schuyler Kaye, created this goofy video illustrating approximately this concept:

My sense is that this kind of personalization is in wide use at places like Amazon, except that their “units of analysis/personalization” are individual tactics (e.g., Gold Box offers), rather than the social influence strategies that can be implemented in many ways and in combination with each other.

What’s interesting about the Rapleaf work described by BusinessWeek is that this enables persuasion profiling even before a service provider or marketer knows anything about you — except that you were referred by or are otherwise connected to a person. This gives them the ability to estimate your persuasion profile by using your social neighborhood, even if you haven’t disclosed this information about your social network.

While there has been some research on individual differences in responses to influence strategies (including when used by computers), as far as I know there isn’t much work on just how much the responses of friends covary. As a tool for influencers online, it doesn’t matter as much whether this variation explained by friends’ responses is also explained by other variables, as long as those variables aren’t available for the influencers to collect. But for us social scientists, it would be interesting to understand the mechanism by which there is this relationship: is it just that friends are likely to be similar in a bunch of ways and these predict our “persuasion profiles”, or are the processes of relationship creation that directly involve these similarities.

This is an exciting and scary direction, and I want to learn more about it.