The designer's influence: social media and the user experience

There's been an interesting conversation underway on IxDA about design and influence. The original post on Johnnyholland also has a few tasty comments. My own take on issue of whether or not designers should be concerned with the ethics of their ability to influence users is, simply, no. But from the perspective of social intraction design, the conversation of design and its influence on users takes an interesting turn. Here's some of what came up today. My comment is in response to Dave Malouf's comment. I thought this might be worth blogging. Is this a cheap way to put up a blog post or another example of email surfacing interesting exchanges? Yes.

For context, Dave's comment, which raises the interesting question of how influence in social media:
"Now, the real question in my mind is to discuss, theorize, etc. HOW to do influence. What about perception and cognition and emotion can we work? What cultural strategies are most effective.
i.e. in social networking design, and social collaboration design there are a ton of means of getting people to be more contributor oriented. This is designing to increase activity.
or in e-commerce models, how do we get more people to hit that final "submit"?
or in health care how do we get people to take better care of themselves, for clinicians to make less mistakes, etc.?" -- Dave Malouf

My response
Why are contributors contributing? Perhaps because they have a sense of the common good, and as motivates many wikipedians, they want to maintain accuracy and breadth of open-sourced knowledge. Or perhaps they're "contributing" to twitter because they've got an enormous ego and no sense of self restraint.

Clearly the term "contribute" loses its meaning very quickly when we get into social media, as nearly everything said or submitted is a contribution: social bookmarking, retweeting, blogging, commenting...

How does one "design" the social -- that's what interests me, and in particular, what kinds of social interactions, individual, interpersonal, social, and public, can be codified? What concepts do we need if we're to go from explaining a single user interaction on social media to the social dynamics of two or more users? Clearly the interactions are users with users, not users with software -- but we cant just use real world social interactions as our models. Mediation strips away face, body, and affect; it removes synchrony of time. Etc etc.. There's plenty more...

So the question of influence is a very good one. It's probably not an ethical one, because "we" don't control the user, his/her perceptions, interests, choices, motives, or his/her experience. Personally i think "framing" may be a viable way to approach the issue of designing the social, as it shifts emphasis from "design" to "perspective", and in social interaction design it's mostly about shaping these nuanced social meanings and negotiations, not functions (as with so much product design or interface design -- and that's not to denigrate style, etc).

The matter does seem v interesting if the question is explored not in terms of our responsibilities as designers but in terms of the user experience: what kinds of users choose to retweet an influencer? What kinds of social incentives work with non-competitive users? Are there ways to reduce the bias or distortion that leader boards often produce? Would there be a way to grow a service like twitter without it turning into a popularity contest for so many users? What social incentives do experts respond to, and could a system be designed to appeal to experts without attracting promoters?

As the motivation is often the other person, the matter of influencing the user does get interesting... Are there ethics involved if a dating site is designed to keep users hopeful, voyeuristically engaged, addicted to checking for new flirts and message, and highly unlikely to get a real date? Dunno, that's the business of dating sites, none of which would survive if they did what the claim to do.

We need to bear in mind that most social media, and perhaps a great deal of software in general, operate in failure mode much of the time. Twitter is not conversational. Followers are not friends. Facebook is not social. Many modern social systems are but a disaster waiting to happen. So how do we talk about influence and incentives if in fact much user activity fails to communicate,is ambiguous in its intent,is redundant with contributions elsewhere, goes un-responded to, is out of context...

If so much of social media interaction is actually handling of failure, responding to breakdown, bridging misunderstanding, and otherwise social "error handling," then perhaps we ought to learn more about what "functional social media" means before worrying that we have too much influence... And i'll say right now that these errors and failures may in fact be the motor of participation on social media: we're into breakdowns, ambiguities, ambivalence, conflict, and drama.

--Adrian

A conversation with Thomas Vander Wal

The following is a raw and unedited email exchange between myself and Thomas Vander Wal (@vanderwal), fellow social interaction designer and social software architect par excellance. Thomas and I have complementary perspectives on social media design and on the methods that best support emergent and managed social practices on social media. Our interest in the design challenge presented by social software and our inclination to new paradigms and concepts is shared. We take different perspectives on how to articulate social interaction models, and this short exchange sheds some unfiltered light on those differences. We thought it would be a pity to lose this email exchange to the dustbin of backchannel history.

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On Jul 21, 2009, at 8:59 AM, Thomas Vander Wal wrote:

The conversations models & how they map to the difference faces & steps in the communication progression from personal, collective, community/group, and collaborative have interest to me. Each are different design problems with very different interaction & communication needs, hence leading to different conversation models.

Personal: Focussed on holding on to objects (including people & relationships) and annotating for refinding and aggregating as needed.

Collective: Open sharing/stating around objects (with various possibilities around level of sociality) with some conversation directly with them in comments, but also indirect conversations (friendfeed, microsharing, etc.)

Community/Group: Fully aware of others with interests around the object and interacting with the others in a manner that is open to others in the community/group.

Collaborative: Goal is getting down to one view and one product. This requires the means to identify and work through conflicting concepts and understanding. Requires working together and identifying, addressing, and working through conflict to come to one resolutions (there can not be more then one personal day policy in an organization).

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On Jul 21, 2009, at 12:25 PM, adrian chan wrote:

these are cats used by ross, clay and others that i'm not totally aligned with. primarily because I dont think they reflect anthropological or sociological distinctions in interaction systems or situations. (e.g. paired interactions, triangulated interactions, group membership, inter-group interaction, alliance, family, tribe, community, or now the social media-specific formations which seem to be "invisible audiences," "publics" or "audiences" depending on who you talk to.)

for example i dont think "collective" is a natural social phenomenon but if it occurs is a byproduct or outcome of carefully structured interactions in which personal social dimensions are minimized to reduce the bias of status, rank, hierarchy and other attention-getting behaviors. Which is why Hunch.com has shirky written all over it, or why we all use wikipedia as our reference standard for collective action!

in other words,
a structuralist would tell us that these categories dont exist.

a sociologist would say that forms of communication and social practices transcend these categories and may be found in the reproduction in any of these categories, so cant be the causal explanation for how these categories of content production are realized.

a psychologist would say that user motives are not a reflection of a kind of social arrangement, that for example interpersonal stuff, attractions and flirting, lurking etc can all occur in social groups of different sizes and structure

a social media theorist might say that it matters more how people see others, see themselves, and think they see how they are seen by othres, and that the constraints on action in and results out are what govern behavior -- but that users wont have "collective" or "collaboration" etc in mind when they're acting -- that user centric view will prevail over an architectural one

i think where shirky has a blindspot is in motives -- he's a good pattern recognizer but patterns can be effects without being causes, or without being the goal or the motive of a certain user's activity.

where shirky sees structure as a way of possibly eliminating social distortions, i still think it's essential to know how the user sees himself in the social field to know where bias may be introduced.

and in today's highly conversational mediaverse, these structures are hard to map to aggregation, disaggregation, and other twitter/status feed phenomena. twitter and its kin are so fluid, so ephemeral and time-based, that it's hard to grasp the causes of social outcomes without using communication theory and interaction dynamics (which i sloppily call "conversation models"). challenge being that one has to capture what interests a user -- could be their own status, could be their reputation, their commitment to a higher goal, their need for attention, etc, all of which come out in conversation but none of which are governed by structural arrangements (like collab, collective, or community)....

in short the question you raise is: does the social order account for user behavior? Is the social order the user's orientation. I dont think it is, but that would be my bone to pick with ross or shirky (some day....)

what do you think? am i making sense?

a

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On Jul 21, 2009, at 9:52 AM, Thomas Vander Wal wrote:

Your approach makes sense and fits wonderfully within social comfort. One of the things I have found working with organizations on the inside is the assumptions from the outside (open web tools) are broken. Adoption of the same patterns outside don't happen inside organizations, as the measures are vastly different (outside pure numbers (100k to millions of users) and inside is percentage of employees/customers). Our assumed understanding for tools and models from web 2.0 don't really work well when dealing with closed populations. What we realize is these tools are less than optimal on the web too. This was my huge problem in writing my book (Understanding Folksonomy) for O'Reilly, I could not explain value that was derived nor could I explain things that were broken.

Conversation models fit nicely in social comfort, which I currently have set within the elements of social software and build order. Unless the prior elements are met, there is no communication/conversation. The realm of social is far more complex and runs on many different planes and models at once. There is no pure model, but a mixture of models and understandings.

The elements of social software and social comfort are important in all of the faces of perception (where personal, collective, community, collaboration, newbie, system owner, and external developer) come into play as task roles. But, seen from the perspective of a cube or other polygon, we can see many sides at once and are participants in the various tasks and faces.

I agree and disagree with "but that users wont have "collective" or "collaboration" etc in mind when they're acting" as I see the mindset of whom am I sharing with (how broadly) and goals (stated or inferred) with the task type, when users are interacting with others on internal social tools. But, it is not the user's perspective that is at the forefront as much as it is having the proper tools with the proper elements to achieve each type of task. Most organizations do not think of the progression of tasks and ensure their tools embrace the needs at the various stages. Often true collaboration elements are missing as well as desperately needed tools for personal tasks.

SIM Scoring: Social Media Influence Metrics are an Art

Influence metrics are growing up. According to Adage, Razorfish is about to introduce "the SIM score, which stands for social influence marketing." The new score is covered by Abbey Klaassen in What's Your Brand's Social Score?. Social media marketers have long sought (relatively speaking) a standard measure of social media ROI. I don't know that this SIM score is it. Let's have a quick look.

The SIM score is apparently a social media version of the Net Promoter mode. Adage puts it like this: "How likely is it that you would recommend our company to a friend or colleague?" (To get the score, subtract the "highly likelies," or promoters, from the "unlikelies," or detractors.)

And according to the article, the Razorfish SIM score seeks to capture the strength of social media as a medium for organically surfacing recommendations. Quoted in Adage, Shiv Singh, VP-global social media lead at Razorfish also recognizes what many social media marketers have long known: the conversation is out there (like it or not):

"Any mention of a brand, as long as it's not negative, serves a brand-awareness purpose on the web because once it's there, it stays there."

The score comprises of a net measure of sentiment as captured in social media mentions. Again, from Adage:
Razorfish worked with TNS/Cymfony to capture social media content and the net sentiment of a brand: the positive and neutral conversations minus negative ones, divided by total conversations about the brand.

As most folks in the social media analytics space know, as I'm sure is familiar at Razorfish and Cymfony, social media do not make it easy to obtain sentiment and semantic metrics. There are several reasons for this, some of which are specific to the medium and some of which are behavioral:

• The 140 character limit on tweets puts significant pressure on context. Context is often left out of tweets where it can be assumed by the reader. Crawlers of course have difficulty recognizing the implicit references and context of tweets, so some if not many tweets are simply missed.


• Expressions in twitter are colloquial, if not also abbreviated, shortened, and clipped. Again, expressions often don't explicitly reference topics and content (brands, industries, products included).


• People make recommendations in twitter shaped in part by who they follow and who's following them. One can't remove the act of recommending from the audience the recommendation is made to or in front of. People will often make recommendations not only to share their feelings about a product/brand, but also to publicly identify with that product or brand. References made in social media like twitter reflect on the twitterer. Tweets can show a person identifying with something or someone, attracting the attention of someone, showing gratitude to someone, showing affection for someone, and so on.


• In public social media like twitter, a recommendation may also serve the purpose of building a person's credibility or reputation as an expert, influencer, trusted authority, and so on. Consider the difference in recommendations made by @Scobleizer and @GuyKawasaki and @jowyang. Each of these heavy users and influencers has his own way of watching for, filtering, selecting and then tweeting or retweeting. @guykawasaki has influence as a newswire, more than @jowyang, whose influence rests more on his personal and professional authority.


• Recommendations can come as answers to solicited or unsolicited requests for help or information.


• Recommendations may be made as a means of introduction on twitter -- sometimes to get followed back, to get noticed, or simply to be helpful.

These are some of the ways in which recommendations might be distinguished in social media from recommendations made face to face or by other means (as measured by the Net Promoter method). In conversational media, the act of communicating is difficult to separate from the information communicated. Recommendations and the act of recommending can be measured differently, and have different meanings: the intention behind the act, the message or information provided, motives inferred by recipient to the act. (Person A tells person B to go see Harry Potter, hoping to get the question "Oh you saw it?! Was it good?" and instead Person B ignores Person A, wondering to herself "Why is A telling me to see Harry Potter? Don't they know it's not my kind of thing?")

There are also ways in which recommendations may elude attempts to simplify sentiment captured from social media. There are also ways in which social media provide information about a brand's "influence" that are not in what people say but in how they say it, to whom, and what happens when they do. Some of this is what we can call "envelope" information (tweet addressing: to whom, for whom, citing whom, or @name, @reply, RT).

The rest of it is in the distribution: reach, volume, velocity, acceleration. These are aspects of flow and are among the attributes captured by some social media analytics tools. In marketing speak:

• How quickly is brand retweeted?


• Who retweets?


• How deep down a social graph does the retweeting go?


• How far across a social network does the retweeting go?


• and so on

I know that these aspects of social media activity are difficult to track and measure. But it would be great if there were an industry-wide effort to define and codify some of the attributes of social networks, relationship-based communications, and common types of expression in order to better represent conversational activity in social media. The results would not only paint a more accurate picture of brand presence in social media, but would also match the real social mechanics and dynamcis of online conversations. It may take a while for algorithms and tools to emerge for this. In the meantime, I would supplement SIM scoring with insight from a good community manager.

No Twitter in Meme-tracking and News Cycle Research

Interesting research was reported recently in the New York Times about the relationship between blogs and mass media. The research, out of Cornell University, focused on the news cycles observed in mass media and in the blogosphere. News cycles were measured by the distribution of a "meme." Memes were defined by the researchers as quotes -- full or partial. The research observed a tight 2.5 hour echo, memes peaking rapidly first in the mass media and then in the blogosphere shortly thereafter.

The research pretty much stands for itself, and while the researchers claim to have found a faster peak and decay in the lifecycle of memes than may have been expected, there don't seem to be many surprises in their overall findings. Having defined a news cycle meme as a quote, it's not surprising that quote-able news peaks in a meme like fashion. After all quotes are quote-able and are easily tracked (the research was limited to recognizable variants on the original quote: hence it excluded other kinds of discourse). Quotes can be repeated with limited analysis and context: a quote speaks for itself. Quotes are a common thread in news, insofar as quotes are the juicy bits of what our politicians and celebrities have to say.

It is no surprise that blogs would pick up on these memes soon after their appearance in mass media. Many blogs serve as news sources themselves. And news blogs need news to blog about: news is no different in mass and social media. Nor should the news cycle be much different from one medium to the other.

That said, there might be other cycles unique to social media that would be different. Unfortunately the research doesn't cover these. Research didn't directly address conversational (realtime) social media, most importantly Twitter. Twitter poses some challenges to media research: posts of 140 characters lose context, are reworded, shortened, and otherwise corrupted in ways that make them difficult to relate reliably to source quotes and memes. I think we could comfortably assume that twitter echoes the news cycle in ways like the blogosphere, although faster, and often preceding the mass media. The appearance of twitter-sourced stories in mass media, then through to blogs, has been covered already (e.g. earthquakes, Iran protests).

There are a few reasons social media would make an interesting distinct study, were it possible to reliably constrain research. Social media are more than news media, and that they are frequently driven by talk, interaction, or conversation in the form of tweets, comments, and status updates. Were it possible to conduct the research, it would be interesting to know:

• Is there a long tail distribution of information in conversational (realtime) social media?


• Does the distribution of information in conversational social media tell us something about relationships of credibility, influence, trust, authority, intimacy, etc and how they facilitate the distribution of information?


• Are cycles of information distribution in conversational social media more "organic": subject perhaps to daily rhythms of users and their habits and routines of use?


• Does the "imitation" of information cited by researchers as one of two key ingredients function differently in conversational social media? Specifically, can it be determined whether or not imitation reflects social motives: retweeting for attention; retweeting for association; retweeting to get attention; tweeting for influence; tweeting for social inclusion; and so on.


• After a news quote decays, is there long tail pickup in social media that reflects depth of interest? Can the commenting depth (not addressed by the research but often used by analytics tools) expose a kind of media authority more participatory than mass media, and credible for insight, commentary, analysis and not just news.


• Is there a social graph ingredient in the distribution of news in conversational social media that is not explained by the echoing of news stories but which might offer valuable insight into lines of influence and which could render social relations of different kinds? Motivated not just to report the news, but to associate oneself, identify with a person, event, or to help tell a story, conversationalists can show us who they talk to, and about what.

Excerpts from Meme-tracking and the Dynamics of the News Cycle

Tracking new topics, ideas, and memes across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days the time scale at which we perceive news and events.
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As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cyclethe daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis. We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle. In particular, we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively, with divergent behavior around the overall peak and a heartbeat-like pattern in the handoff between news and blogs.
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First, the set of distinctive phrases shows significant diversity over short periods of time, even as the broader vocabulary remains relatively stable. As a result, they can be used to dissect a general topic into a large collection of threads or memes that vary from day to day. Second, such distinctive phrases are abundant, and therefore are rich enough to act as tracers for a large collection of memes; we therefore do not have to restrict attention to the much smaller collection of memes that happen to be associated with the appearance and disappearance of a single named entity.
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From an algorithmic point of view, we consider these distinctive phrases to act as the analogue of genetic signatures for different memes. And like genetic signatures, we find that while they remain recognizable as they appear in text over time, they also undergo significant mutation.
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Outside of computer science, the interplay between technology, the news media, and the political process has been a focus of considerable research interest for much of the past century [6, 22]. This research tradition has included work by sociologists, communication scholars, and media theorists, usually at qualitative level exploring the political and economic contexts in which news is produced [19], its effect on public opinion , and its ability to facilitate either polarization or consensus [15].
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We perform this analysis both at a global level understanding the temporal variation as a wholeand at a local level identifying recurring patterns in the growth and decay of a meme around its period of peak intensity.
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We also show how the temporal patterns we observe arise naturally from a simple mathematical model in which news sources imitate each others decisions about what to cover, but subject to recency effects penalizing older content.
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Among the fastest sources we find a number of popular political blogs; this measure thus suggests a way of identifying sites that are regularly far ahead of the bulk of media attention to a topic.
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Some of the key research issues here have been the identification of topics over time [5, 11, 16], the evolving practices of bloggers [25, 26], the cascading adoption of stories [3, 14, 20, 23], and the ideological divisions in the blogosphere [2, 12, 13].
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Our goal is to produce phrase clusters, which are collections of phrases deemed to be close textual variants of one another. We will do this by building a phrase graph where each phrase is represented by a node and directed edges connect related phrases. Then we partition this graph in such a way that its components will be the phrase clusters.
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Notice how the plot captures the dynamics of the presidential campaign coverage at a very fine resolution. Spikes and the phrases pinpoint the exact events and moments that triggered large amounts of attention.
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To begin with, there are interesting potential analogies to natural systems that contain dynamics similar to what one sees in the news cycle. For example, one could imagine the news cycle as a kind of species interaction within an ecosystem [18], where threads play the role of species competing for resources (in this case media attention, which is constant over time), and selectively reproducing (by occupying future articles and posts).
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We argue that in formulating a model for the news cycle, there are two minimal ingredients that should be taken into account. The first is that different sources imitate one another, so that once a thread experiences significant volume, it is likely to persist and grow through adoption by others. The second, counteracting the first, is that threads are governed by strong recency effects, in which new threads are favored to older ones.
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When there is only a recency effect but no imitation (so the probability of choosing thread j is proportional only to (t - tj) for some function ), we see that no thread ever achieves significant volume, since each is crowded out by newer ones.
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When there is only imitation but no recency effect, (so the probability of choosing thread j is proportional only to f(nj) for some function f), then a single thread becomes dominant essentially forever: there are no recency effects to drive it away, although its dominance shrinks over time simply because the total number of competing threads is increasing.
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In general, one would expect the overall volume of a thread to be very low initially; then as the mass media begins joining in the volume would rise; and then as it percolates to blogs and other media it would slowly decay. However, it seems that the behavior tends to be quite different from this. First, notice that in Figure 7 the rise and drop in volume is surprisingly symmetric around the peak, which suggests little or no evidence for a quick build-up followed by a slow decay.
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The majority of phrases first appear in news media and then diffuses to blogs where it is then discussed for longer time. However, there are also phrases that propagate in the opposite way, percolating in the blogosphere until they are picked up the news media. Such cases are very important as they show the importance of independent media.

Authors: Jure Leskovec Lars Backstrom Jon Kleinberg

New York Times article about the news cycle research
Study Measures the Chatter of the News Cycle

Reflections on the Techcrunch Realtime Crunchup

Last Friday was the Techcrunch Crunchup conference about the Realtime web. It was followed immediately by the annual TC party at August Capital. Of the many conferences I've attended, Crunchup was more interesting and relevant than most. It kicked off on a high note with a discussion between Ron Conway and John Borthwick, who were pestered for financial secrets by Michael Arrington and the occasional bark from Steve Gillmor.

Why Steve must stand alongside a panel every panel I've seen him at escapes me -- unless it is to exercise his signature interrogatives from sidelines at a distance from which it is safer to lob remarks. Neither Mike nor Steve are the most supportive interlocutors you've seen on stage. But the tech community sets its bar for live interaction pretty low... I digress...

I wanted to capture a couple reflections on the day's demos and panels. Without naming companies by name, there was a clear preponderance of feed readers and aggregators. It struck me that the industry's first response to the realtime web is to address disaggregation of conversation forced by the many sites, services, and practices that currently produce and support conversation. (Think commenting, status updates, twitter, and the fact that most of these can be commented on at their source, on sites they are syndicated to, or even in some third party apps).

In other words the problem created by the realtime web, and addressed by many of these apps, is an information problem. Clay Shirky has been on top of this of late, arguing that the realtime web creates an abundance of personal, social, and emotional news and content. Robert Scoble, whose behavior and lifestyle often speaks for itself, in volumes, gets the flow filtration problem.

Clay sees the distortive human bias that can creep into a pattern of social media use. Scoble sees the patterns in the content (earthquakes, Michael Jackson). For Shirky the pattern is in mediated audiences; for Scoble the pattern is in the content they circulate (news).

Both approaches clearly work at some level to reduce the complexity of massive amounts of conversational content. But the content produced by people talking on twitter, declaring their status in Facebook, or liking and commenting in Friendfeed is not just content like any other kind of content. It's talk: often addressed to one or more people, or at least shared with an audience in mind (followers). Most tweets make some kind of appeal that audience: appealing for a response (RT, @reply), for a follow, a comment, a click. If not in the tweet itself, the practice of tweeting in general begs for social validation and acknowledgment.

In other words the realtime web, insofar as its content is the content of a strange kind of talk, is not the same kind of content as regular web content. It's neither news nor information -- though it may be news and it may be informative. Those forms of content hail from broadcast media. I would insist that talk produced by people aware to some degree of how they appear to others, engaged in the activity of maintaining a reputation, of sustaining relationships, and of talking in an interested way, leave behind content that's of a different nature than the stuff that comes across the newswire.

Or just think subjective vs objective.

Point being that realtime web content issues may be addressed by filtering and aggregating (or re-aggregating, as JS-Kit calls it) incoming feeds to reduce noise and increase relevance. But does this approach leave the conversationality of realtime web interactions unaddressed?

The realtime web may mean access to social news (twitter, Facebook, etc) in realtime. A shift to realtime flows and content streams. To news and updates, and to the trends that will naturally accompany real-time distribution. But realtime access is not the same as synchrony. Synchrony is the simultaneity of two or more independent actors -- synchrony in conversation allows us to coordinate communication. We all know that twitter is "realtime" but asynchronous. We get content in realtime, but don't assume that others are in twitter at the same time.

Is this emphasis on the information problem of realtime just a common-sense approach, given that it's an extension of conventional web search, filtering, sorting and results display practices? Or will there be another wave of companies -- those that treat realtime web as a conversation space, focusing perhaps on presence, availability, relationship, similarity, affinity, graph and so on?

What's in the Venn overlap of these two approaches, where content is people but people make content, and where affinities can produce trust, confidence, similarity of identity, interest, communication, and sometimes relationships? I didn't see much by way of social browsing, social serendipity or discovery. Personally I don't benefit as much from realtime content as I do from tweeting with friends.

I want to know more about the latent connections and relationships between people. When I'm realtime I drive, I do not use readers but tend instead to browse when I'm interested in finding something out. Constant exposure to realtime social commentary paralyzes me. Most panelists of course acknowledged that we're in early days yet. The easiest problems to solve are indeed aggregation, filtering, and search -- and that's not to diminish the engineering or user experience challenges there.

I'm fairly certain that the treasure in realtime web is not in content but in the relationships, in the meaning of content as revealing disclosures of trusted, influential, respected, credible, authoritative, and liked relationships among friends, peers, and strangers. ...Of course that content surfaces only if the tools supporting online social interaction preserve the human and personal aspects of the user experience.