Selecting effective means to any end
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] Aarts, E.H.L., Markopoulos, P., Ruyter, B.E.R.: The persuasiveness of ambient intelligence. In: Petkovic, M., Jonker, W. (eds.) Security, Privacy and Trust in Modern Data Management. Springer, Heidelberg (2007)
[2] Baker, S.: Learning, and profiting, from online friendships. BusinessWeek 9(22) (May 2009)Selecting Effective Means to Any End 93
[3] Berdichevsky, D., Neunschwander, E.: Toward an ethics of persuasive technology. Commun. ACM 42(5), 51–58 (1999)
[4] Cacioppo, J.T., Petty, R.E., Kao, C.F., Rodriguez, R.: Central and peripheral routes to persuasion: An individual difference perspective. Journal of Personality and Social Psychology 51(5), 1032–1043 (1986)
[5] Cialdini, R.: Influence: Science and Practice. Allyn & Bacon, Boston (2001)
[6] Cohn,D.A., Ghahramani,Z.,Jordan,M.I.:Active learning with statistical models. Journal of Artificial Intelligence Research 4, 129–145 (1996)
[7] Eckles, D.: Redefining persuasion for a mobile world. In: Fogg, B.J., Eckles, D. (eds.) Mobile Persuasion: 20 Perspectives on the Future of Behavior Change. Stanford Captology Media, Stanford (2007)
[8] Fogg, B.J.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann, San Francisco (2002)
[9] Fogg, B.J.: Protecting consumers in the next tech-ade, U.S. Federal Trade Commission hearing (November 2006), http://www.ftc.gov/bcp/workshops/techade/pdfs/transcript_061107.pdf
[10] Fogg,B.J.:The behavior grid: 35 ways behavior can change. In: Proc. of Persuasive Technology 2009, p. 42. ACM, New York (2009)
[11] Kaptein, M., Aarts, E.H.L., Ruyter, B.E.R., Markopoulos, P.: Persuasion in am- bient intelligence. Journal of Ambient Intelligence and Humanized Computing 1, 43–56 (2009)
[12] Lacroix, J., Saini, P., Goris, A.: Understanding user cognitions to guide the tai- loring of persuasive technology-based physical activity interventions. In: Proc. of Persuasive Technology 2009, vol. 350, p. 9. ACM, New York (2009)
[13] Petty, R.E., Wegener, D.T.: The elaboration likelihood model: Current status and controversies. In: Chaiken, S., Trope, Y. (eds.) Dual-process theories in social psychology, pp. 41–72. Guilford Press, New York (1999)
[14] Prabhaker, P.R.: Who owns the online consumer? Journal of Consumer Market- ing 17, 158–171 (2000)
[15] Rawls, J.: The independence of moral theory. In: Proceedings and Addresses of the American Philosophical Association, vol. 48, pp. 5–22 (1974)
[16] Rhoads, K.: How many influence, persuasion, compliance tactics & strategies are there? (2007), http://www.workingpsychology.com/numbertactics.html
[17] Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1/2), 115–153 (2001)
- 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. [↩]
- 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. [↩]
Ethical persuasion profiling?
Persuasion profiling — estimating the effects of available influence strategies on an individual and adaptively selecting the strategies to use based on these estimates — sounds a bit scary. For many, ‘persuasion’ is a dirty word and ‘profiling’ generally doesn’t have positive connotations; together they are even worse! So why do we use this label?
In fact, Maurits Kaptein and I use this term, coined by BJ Fogg, precisely because it sounds scary. We see the potential for quite negative consequences of persuasion profiling, so we try to alert our readers to this.1
On the other hand, we also think that, not only is persuasion profiling sometimes beneficial, but there are cases where choosing not to adapt to individual differences in this way might itself be unethical.2 If a company marketing a health intervention knows that there is substantial variety in how people respond to the strategies used in the intervention — such that while the intervention has positive effects on average, it has negative effects for some — it seems like they have two ethical options.
First, they can be honest about this in their marketing, reminding consumers that it doesn’t work for everyone or even trying to market it to people it is more likely to work for. Or they could make this interactive intervention adapt to individuals — by persuasion profiling.
Actually for the first option to really work, the company needs to at least model how these responses vary by observable and marketable-to characteristics (e.g., demographics). And it may be that this won’t be enough if there is too much heterogeneity: even within some demographic buckets, the intervention may have negative effects for a good number of would-be users. On the other hand, by implementing persuasion profiling, the intervention will help more people, and the company will be able to market it more widely — and more ethically.
A simplified example that is somewhat compelling to me at least, but certainly not airtight. In another post, I’ll describe how somewhat foreseeable, but unintended, consequences should also give one pause.
- You might say we tried to build in a warning for anyone discussing or promoting this work. [↩]
- We argue this in the paper we presented at Persuasive Technology 2010. The text below reprises some of what we said about our “Example 4” in that paper.
Kaptein, M. & Eckles, D. (2010). Selecting effective means to any end: Futures and ethics of persuasion profiling. Proceedings of Persuasive Technology 2010, Lecture Notes in Computer Science. Springer. [↩]
Traits, adaptive systems & dimensionality reduction
Psychologists have posited numerous psychological traits and described causal roles they ought to play in determining human behavior. Most often, the canonical measure of a trait is a questionnaire. Investigators obtain this measure for some people and analyze how their scores predict some outcomes of interest. For example, many people have been interested in how psychological traits affect persuasion processes. Traits like need for cognition (NFC) have been posited and questionnaire items developed to measure them. Among other things, NFC affects how people respond to messages with arguments for varying quality.
How useful are these traits for explanation, prediction, and adaptive interaction? I can’t address all of this here, but I want to sketch an argument for their irrelevance to adaptive interaction — and then offer a tentative rejoinder.
Interactive technologies can tailor their messages to the tastes and susceptibilities of the people interacting with and through them. It might seem that these traits should figure in the statistical models used to make these adaptive selections. After all, some of the possible messages fit for, e.g., coaching a person to meet their exercise goals are more likely to be effective for low NFC people than high NFC people, and vice versa. However, the standard questionnaire measures of NFC cannot often be obtained for most users — certainly not in commerce settings, and even people signing up for a mobile coaching service likely don’t want to answer pages of questions. On the other hand, some Internet and mobile services have other abundant data available about their users, which could perhaps be used to construct an alternative measure of these traits. The trait-based-adaptation recipe is:
- obtain the questionnaire measure of the trait for a sample,
- predict this measure with data available for many individuals (e.g., log data),
- use this model to construct a measure for out-of-sample individuals.
This new measure could then be used to personalize the interactive experience based on this trait, such that if a version performs well (or poorly) for people with a particular score on the trait, then use (or don’t use) that version for people with similar scores.
But why involve the trait at all? Why not just personalize the interactive experience based on the responses of similar others? Since the new measure of the trait is just based on the available behavioral, demographic, and other logged data, one could simply predict responses based on those measure. Put in geometric terms, if the goal is to project the effects of different message onto available log data, why should one project the questionnaire measure of the trait onto the available log data and then project the effects onto this projection? This seems especially unappealing if one doesn’t fully trust the questionnaire measure to be accurate or one can’t be sure about which the set of all the traits that make a (substantial) difference.
I find this argument quite intuitively appealing, and it seems to resonate with others.1 But I think there are some reasons the recipe above could still be appealing.
One way to think about this recipe is as dimensionality reduction guided by theory about psychological traits. Available log data can often be used to construct countless predictors (or “features”, as the machine learning people call them). So one can very quickly get into a situation where the effective number of parameters for a full model predicting the effects of different messages is very large and will make for poor predictions. Nothing — no, not penalized regression, not even a support vector machine — makes this problem go away. Instead, one has to rely on the domain knowledge of the person constructing the predictors (i.e., doing the “feature engineering”) to pick some good ones.
So the tentative rejoinder is this: established psychological traits might often make good dimensions to predict effects of different version of a message, intervention, or experience with. And they may “come with” suggestions about what kinds of log data might serve as measures of them. They would be expected to be reusable across settings. Thus, I think this recipe is nonetheless deserves serious attention.
- I owe some clarity on this to some conversations with Mike Nowak and Maurits Kaptein. [↩]
Persuasion profiling and genres: Fogg in 2006
Maurits Kaptein and I have recently been thinking a lot about persuasion profiling — estimating and adapting to individual differences in responses to influence strategies based on past behavior and other information. With help from students, we’ve been running experiments and building statistical models that implement persuasion profiling.
My thinking on persuasion profiling is very much in BJ Fogg’s footsteps, since he has been talking about persuasion profiling in courses, lab meetings, and personal discussions since 2004 or earlier.
Just yesterday, I came across this transcript of BJ’s presentation for an FTC hearing in 2006. I was struck at how much it anticipates some of what Maurits and I have written recently (more on this later). I’m sure I watched the draft video of the presentation back then and it’s influenced me, even if I forgot some of the details.
Here is the relevant excerpt from BJ’s comments for the FTC:
Persuasion profiling means that each one of us has a different set of persuasion strategies that affect us. Just like we like different types of food or are vulnerable to giving in to different types of food on a diet, we are vulnerable to different types of persuasion strategies.
On the food example, I love old-fashioned popcorn, and if I go to a party and somebody has old-fashioned popcorn, I will probably break down and eat it. On the persuasion side of things, I know I’m vulnerable to trying new things, to challenges and to anything that gets measured. If that’s proposed to me, I’m going to be vulnerable and I’m going to give it a shot.
Whenever we go to a Web site and use an interactive system, it is likely they will be capturing what persuasion strategies work on us and will be using those when we use the service again. The mapping out of what makes me tick, what motivates me can also be bought or sold, just like a credit report.
So imagine I’m going in to buy a new car and the person selling me the car downloads my credit report but also buys my persuasion profile. I may or may not know about this. Imagine if persuasion profiles are available on political campaigns so that when I visit a Web site, the system knows it is B.J. Fogg, and it changes [its] approach based on my vulnerabilities when it comes to persuasion.
Persuasive technology will touch our lives anywhere that we access digital products or services, in the car, in our living room, on the Web, through our mobile phones and so on. Persuasive technology will be all around us, and unlike other media types, where you have 30-second commercial or a magazine ad, you have genres you can understand, when it comes to computer-based persuasion, it is so flexible that it won’t have genre boundaries. It will come to us in the ordinary course of our lives, as we are working on a Web site, as we are editing a document, as we are driving a car. There won’t be clear markers about when you are being persuaded and when you are not.
This last paragraph is about the “genrelessness” of many persuasive technologies. This isn’t directly on the topic of persuasion profiling, but I see it as critically relevant. Persuasion profiling is likely to be most effective when invisible and undisclosed to users. From this and the lack of genre-based flags for persuasive technology it follows that we will frequently be “persuasion profiled” without knowing it.
Using social networks for persuasion profiling
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.