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. [↩]

In the film The Descendents, George Clooney’s character Matt King wrestles — sometimes comically — with new and old choices involving his family and Hawaii. In one case, King decides he wants to meet a rival, both just to meet him and to give him some news; that is, he (at least explicitly) has generally good reason to meet him. Perhaps he even ought to meet him. When he actually does meet him, he cannot just do these things, he also argues with his rival, etc. King’s unplanned behaviors end up causing his rival considerable trouble.¹

This struck me as related to some challenges in formulating what one should do — that is, in the “practical reasoning” side of ethics.

One way of getting practical advice out of virtue ethics is to say that one should do what the virtuous person would do in this situation. On its face, this seems right. But there are also some apparent counterexamples. Consider a short-tempered tennis player who has just lost a match.² In this situation, the virtuous person would walk over to his opponent, shake his hand, and say something like “Good match.” But if this player does that, he is likely to become enraged and even assault his victorious opponent. So it seems better for him to walk off the court without attempting any of this — even though this is clearly rude.

The simple advice to do what the virtuous person would do in the present situation is, then, either not right or not so simple. It might be right, but not so simple to implement, if part of “the present situation” is one’s own psychological weaknesses. Aspects of the agent’s psychology — including character flaws — seem to license bad behavior and to remove reasons for taking the “best” actions.

King and other characters in The Descendents face this problem, both in the example above and at some other points in the movie. He begins a course of action (at least in part) because this is what the virtuous person would do. But then he is unable to really follow through because he lacks the necessary virtues.³ We might take this as a reminder of the ethical value to being humble — to account for our faults — when reasoning about what we ought to do.⁴ It is also a reminder of how frustrating this can be, especially when one can imagine (and might actually be able to) following through on doing what the virtuous person would do.

One way to cope with these weaknesses is to leverage other aspects of one’s situation. We can make public commitments to do the virtuous thing. We can change our environment, sometimes by binding our future selves, like Ulysses, from acting on our vices once we’ve begun our (hopefully) virtuous course of action. Perhaps new mobile technologies will be a substantial help here — helping us intervene in our own lives in this way.

1. Perhaps deserved trouble. But this certainly didn’t play a stated role in the reasoning justifying King’s decision to meet him. [↩]
2. This example is first used by Gary Watson (“Free Agency”, 1975) and put to this use by Michael Smith in his “Internalism” (1995). Smith introduces it as a clear problem for the “example” model of how what a virtuous person would do matters for what we should each do. [↩]
3. Another reading of some of these events in The Descendents is that these characters actually want to do the “bad behaviors”, and they (perhaps unconciously) use their good intentions to justify the course of action that leads to the bad behavior. [↩]
4. Of course, the other side of such humility is being short on self-efficacy. [↩]

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.

1. You might say we tried to build in a warning for anyone discussing or promoting this work. [↩]
2. 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. [↩]

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:

1. obtain the questionnaire measure of the trait for a sample,
2. predict this measure with data available for many individuals (e.g., log data),
3. 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.

1. I owe some clarity on this to some conversations with Mike Nowak and Maurits Kaptein. [↩]

I’ve written previously about how filtered activity streams [edit: i.e. news feeds] can lead to biased views of behaviors in our social neighborhoods. Recent conversations with two people writing popular-press books on related topics have helped me clarify these ideas. Here I reprise previous comments on filtered activity streams, aiming to highlight how they apply even in the case of simple and transparent personalization rules, such as those used by Twitter.
—

Birds of a feather flock together. Once flying together, a flock is also subject to the same causes (e.g., storms, pests, prey). Our friends, family, neighbors, and colleagues are more similar to us for similar reasons (and others). So we should have no illusions that the behaviors, attitudes, outcomes, and beliefs of our social neighborhood are good indicators of those of other populations — like U.S. adults, Internet users, or homo sapiens of the past, present, or future. The apocryphal Pauline Kael quote “How could Nixon win? No one I know voted for him” suggests both the ease and error of this kind of inference. I take it as a given that people’s estimates of larger populations’ behaviors and beliefs are often biased in the direction of the behaviors and beliefs in their social neighborhoods. This is the case with and without “social media” and filtered activity streams — and even mediated communication in general.

That is, even without media, our personal experiences are not “representative” of the American experience, human experience, etc., but we do (and must) rely on it anyway. One simple cognitive tool here is using “ease of retrieval” to estimate how common or likely some event is: we can estimate how common something is based on how easy it is to think of. So if something prompts someone to consider how common a type of event is, they will (on average) estimate the event as more common if it is more easy to think of an example of the event, imagine the event, etc. And our personal experiences provide these examples and determine how easy they are to bring to mind. Both prompts and immediately prior experience can thus affect these frequency judgments via ease of retrieval effects.

Now this is not to say that we should think as ease of retrieval heuristics as biases per se. Large classes and frequent occurrences are often more available to mind than those that are smaller or less frequent. It is just that this is also often not the case, especially when there is great diversity in frequency among physical and social neighborhoods. But certainly we can see some cases where these heuristics fail.

Media are powerful sources of experiences that can make availability and actual frequency diverge, whether by increasing the biases in the direction of projecting our social neighborhoods onto larger population or in other, perhaps unexpected directions. In a classic and controversial line of research in the 1970s and 80s, Gerbner and colleagues argued that increased television-watching produces a “mean world syndrome” such that watching more TV causes people to increasingly overestimate, e.g., the fraction of adult U.S. men employed in law enforcement and the probability of being a victim of violent crime. Their work did not focus on investigating heuristics producing these effects, but others have suggested the availability heuristic (and related ease of retrieval effects) as at work. So even if my social neighborhood has fewer cops or victims of violent crime than the national average, media consumption and the availability heuristic can lead me to overestimate both.

Personalized and filtered activity streams certainly also affect us through some of the same psychological processes, leading to biases in users’ estimates of population-wide frequencies. They can aIso bias inference about our own social neighborhoods. If I try to estimate how likely a Facebook status update by a friend is to receive a comment, this estimate will be affected by the status updates I have seen recently. And if content with comments is more likely to be shown to me in my personalized filtered activity stream (a simple rule for selecting more interesting content, when there is too much for me to consume it all), then it will be easier for me to think of cases in which status updates by my friends do receive comments.

In my previous posts on these ideas, I have mainly focused on effects on beliefs about my social neighborhood and specifically behaviors and outcomes specific to the service providing the activity stream (e.g., receiving comments). But similar effects apply for beliefs about other behaviors, opinions, and outcomes. In particular, filtered activity streams can increase the sense that my social neighborhood (and perhaps the world) agrees with me. Say that content produced by my Facebook friends with comments and interaction from mutual friends is more likely to be shown in my filtered activity streams. Also assume that people are more likely to express their agreement in such a way than substantial disagreement. As long as I am likely to agree with most of my friends, then this simple rule for filtering produces an activity stream with content I agree with more than an unfiltered stream would. Thus, even if I have a substantial minority of friends with whom I disagree on politics, this filtering rule would likely make me see less of their content, since it is less likely to receive (approving) comments from mutual friends.

I’ve been casually calling this larger family of effects this the “friendly world syndrome” induced by filtered activity streams. Like the mean world syndrome of the television cultivation research described above, this picks out a family of unintentional effects of media. Unlike the mean world syndrome, the friendly world syndrome includes such results as overestimating how many friends I have in common with my friends, how much positive and accomplishment-reporting content my friends produce, and (as described) how much I agree with my friends.¹

Even though the filtering rules I’ve described so far are quite simple and appealing, they still are more consistent with versions of activity streams that are filtered by fancy relevance models, which are often quite opaque to users. Facebook News Feed — and “Top News” in particular — is the standard example here. On the other hand, one might think that these arguments do not apply to Twitter, which does not apply any kind of machine learning model estimating relevance to filtering users’ streams. But Twitter actually does implement a filtering rule with important similarities to the “comments from mutual friends” rule described above. Twitter only shows “@replies” to a user on their home page when that user is following both the poster of the reply and the person being replied to.² This rule makes a lot of sense, as a reply is often quite difficult to understand without the original tweet. Thus, I am much more likely to see people I follow replying to people I follow than to others (since the latter replies are encountered only from browsing away from the home page. I think this illustrates how even a straightforward, transparent rule for filtering content can magnify false consensus effects.

One aim in writing this is to clarify that a move from filtering activity streams using opaque machine learning models of relevance to filtering them with simple, transparent, user-configurable rules will likely be insufficient to prevent the friendly world syndrome. This change might have many positive effects and even reduce some of these effects by making people mindful of the filtering.³ But I don’t think these effects are so easily avoided in any media environment that includes sensible personalization for increased relevance and engagement.

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 [↩]