Tag: Friendfeed

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End of Feed

This is icon for social networking website. Th...
Friendfeed (Photo credit: Wikipedia) Public Domain

Following on from (but unrelated to) my post last week about feed tools we have two posts, one from Deepak Singh, and one from Neil Saunders, both talking about ‘friend feeds’ or ‘lifestreams’. The idea here is of aggregating all the content you are generating (or is being generated about you?) into one place. There are a couple of these about but the main ones seem to be Friendfeed and Profiliac. See Deepaks’s post (or indeed his Friendfeed) for details of the conversations that can come out of these type of things.

A (small) feeding frenzy – Cameron Neylon, Science in the Open – 10 March 2008

Half the links in that quote are dead. I wrote the post above seven years ago today, and it very much marked a beginning. Friendfeed went on to become the coffee house for a broad community of people interested in Open Science and became the place where, for me at least, many of the key discussions took place. Friendfeed was one of a number of examples of “life feed” services. The original intent was as an aggregation point for your online activity but the feed itself rapidly became the focus. Facebook in particular owes a debt to the user experience of Friendfeed. Facebook bought Friendfeed for the team in 2009 and rapidly started incorporating its ideas.

Yesterday Facebook announced they were going to shutter the service that they have to be fair kept going for many years now with no revenue source and no doubt declining user numbers. Of course those communities that remained are precisely the ones that most loved what the service offered. The truly shocking thing is that although nothing has been done to the interface or services that Friendfeed offers for five years it still remains a best in class experience. Louis Gray had some thoughts on what was different about Friendfeed. It remains, in my view, the best technical solution and user experience for enabling the kind of sharing that researchers actually want to do.  I remember reading about Robert Scoble disliked the way that Friendfeed worked, and thinking “all those things are a plus for researchers…”. Twitter is ok, Facebook really not up to the job, Figshare doesn’t have the social features and all the other “facebooks for science” simply don’t have critical mass. Of course, neither did Friendfeed once everyone left either…but while there was a big community there we had a glimpse of what might be possible.

It’s also a reminder, as discussed in the Principles for Scholarly Infrastructures that Geoff Bilder, Jennifer Lin and myself released a week or so back, that relying on the largesse of third parties is not a reliable foundation to build on. If we want to take care of our assets as a community, we need to take responsibility for them as well. In my view there is some important history buried in the records of Friendfeed and I’m going to make some effort to build an archive. This script appears to do a good job of grabbing public feeds. It doesn’t pull discussions (ie the comments on other people’s posts) unless you have the “remote key” for that account. If anyone wants to send me their remote key (log in to friendfeed and navigate to http://friendfeed.com/remotekey) I’ll take a shot at grabbing their discussions as well. Otherwise I’ll just try and prioritize the most important accounts from my perspective to archive.

Is it recent history or is it ancient? We lost Jean-Claude Bradley last year, one of the original thinkers, and perhaps more importantly do-ers, of many strands in Open Research. Much of his thinking from 2008-2011 was on Friendfeed. For me, it was the space in which the foundations for a lot of my current thinking was laid. And where I met many of the people who helped me lay those foundations. And a lot of my insights into how technology does and does not help communities were formed by watching how much better Friendfeed was than many other services. Frankly a lot of the half-baked crap out there today could learn a lot by looking at how this nearly decade-old website works. And still works for those communities that have stayed in strength.

But that is the second lesson. It is the combination of functionality and the community that makes the experience so rich. My community, the Open Science group, left en masse after Facebook acquired Friendfeed. That community no longer trusted that the service would stay around (c.f. again those principles on trust). The librarian community stayed and had an additional five years of rich interactions. It’s hardly new to say that you need both community and technology working together to build a successful social media experience. But it still makes me sad to see it play out like this. And sad that the technology that demonstrably had the best user experience for research and scholarship in small(ish) communities never achieved the critical mass that it needed to succeed.

 

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Loss, time and money

May - Oct 2006 Calendar
May – Oct 2006 Calendar (Photo credit: Wikipedia)

For my holiday project I’m reading through my old blog posts and trying to track the conversations that they were part of. What is shocking, but not surprising with a little thought, is how many of my current ideas seem to spring into being almost whole in single posts. And just how old some of those posts are. At the some time there is plenty of misunderstanding and rank naivety in there as well.

The period from 2007-10 was clearly productive and febrile. The links out from my posts point to a distributed conversation that is to be honest still a lot more sophisticated than much current online discussion on scholarly communications. Yet at the same time that fabric is wearing thin. Broken links abound, both internal from when I moved my own hosting and external. Neil Saunders’ posts are all still accessible, but Deepak Singh’s seem to require a trip to the Internet Archive. The biggest single loss, though occurs through the adoption of Friendfeed in mid-2008 by our small community. Some links to discussions resolve, some discussions of discussions survive as posts but whole chunks of the record of those conversations – about researcher IDs, peer review, and incentives and credit appear to have disappeared.

As I dig deeper through those conversations it looks like much of it can be extracted from the Internet Archive, but it takes time. Time is a theme that runs through posts starting in 2009 as the “real time web” started becoming a mainstream thing, resurfaced in 2011 and continues to bother. Time also surfaces as a cycle. Comments on peer review from 2011 still seem apposite and themes of feeds, aggregations and social data continue to emerge over time. On the other hand, while much of my recounting of conversations about Researcher IDs in 2009 will look familiar to those who struggled with getting ORCID up and running, a lot of the technology ideas were…well probably best left in same place as my enthusiasm for Google Wave. And my concerns about the involvement of Crossref in Researcher IDs is ironic given I now sit on their board as second representing PLOS.

The theme that travels throughout the whole seven-ish years is that of incentives. Technical incentives, the idea that recording research should be a byproduct of what the researcher is doing anyway and ease of use (often as rants about institutional repositories) appear often. But the core is the question of incentives for researchers to adopt open practice, issues of “credit” and how it might be given as well as the challenges that involves, but also of exchange systems that might turn “credit” into something real and meaningful. Whether that was to be real money wasn’t clear at the time. The concerns with real money come later as this open letter to David Willets suggests a year before the Finch review. Posts from 2010 on frequently mention the UK’s research funding crisis and in retrospect that crisis is the crucible that formed my views on impact and re-use as well as how new metrics might support incentives that encourage re-use.

The themes are the same, the needs have not changes so much and many of the possibilities remain unproven and unrealised. At the same time the technology has marched on, making much of what was hard easy, or even trivial. What remains true is that the real value was created in conversations, arguments and disagreements, reconciliations and consensus. The value remains where it has always been – in a well crafted network of constructive critics and in a commitment to engage in the construction and care of those networks.

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Why the web of data needs to be social

Picture1
Image by cameronneylon via Flickr

If you’ve been around either myself or Deepak Singh you will almost certainly have heard the Jeff Jonas/Jon Udell soundbite: ‘Data finds data. Then people find people’. Jonas is referring to data management frameworks and knowledge discovery and Udell is referring to the power of integrated data to bring people together.

At some level Jonas’ vision (see his chapter[pdf] in Beautiful Data) is what the semantic web ought to enable, the automated discovery of data or objects based on common patterns or characteristics. Thus far in practical terms we have signally failed to make this a reality, particularly for research data and objects.

Udell’s angle (or rather, my interpretation of his overall stance) is more linked to the social web – the discovery of common contexts through shared data frameworks. These contexts might be social groups, as in conventional social networks, a particular interest or passion, or – in the case of Jon’s championing of the iCalendar standard –  a date and place as demonstrated by the  the elmcity project supporting calendar curation and aggregation. Shared context enables the making of new connection, the creation of new links. But still mainly links between people.

It’s not the scientists who are social; it’s the data – Neil Saunders

The naïve analysis of the success of consumer social networks and the weaknesses of science communication has lead to efforts that almost precisely invert the Jonas/Udell concept. In the case of most of these “Facebooks for Scientists” the idea is that people find people, and then they connect with data through those people.

My belief is that it is this approach that has led to the almost complete failure of these networks to gain traction. Services that place the object  research at the centre; the reference management and bookmarking services, to some extent Twitter and Friendfeed, appear to gain much more real scientific use because they mediate the interactions that researchers are interested in, those between themselves and research objects. Friendfeed in particular seems to support this discovery pattern. Objects of interest are brought into your stream, which then leads to discovery of the person behind them.  I often use Citeulike in this mode. I find a paper of interest, identify the tags other people have used for it and the papers that share those tags. If these seems promising, I then might look at the library of the person, but I get to that person through the shared context of the research object, the paper, and the tags around that object.

Data, data everywhere, but not a lot of links – Simon Coles

A common complaint made of research data is that people don’t make it available. This is part of the problem but increasingly it is a smaller part. It is easy enough to put data up that many researchers are doing so, in supplementary data of journal articles, on personal websites, or on community or consumer sites. From a linked data perspective we ought to be having a field day with this, even if it represents only a small proportion of the total. However little of this data is easily discoverable and most of it is certainly not linked in any meaningful way.

A fundamental problem that I feel like I’ve been banging on about for years now is that dearth of well built tools for creating these links. Finally these tools are starting to appear with Freebase Gridworks being an early example. There is a good chance that it will become easier over time for people to create links as part of the process of making their own record. But the fundamental problems we always face, that this is hard work, and often unrewarded work, are limiting progress.

Data friends data…then knowledge becomes discoverable

Human interaction is unlikely to work at scale. We are going to need automated systems to wire the web of data together. The human process simply cannot keep up with the ongoing annotation and connection of data at the volumes that are being generated today. And we can’t afford not to if we want to optimize the opportunities of research to deliver useful outcomes.

When we think about social networks we always place people at their centre. But there is nothing to stop us replacing people with data or other research objects. Software that wants to find data, data that wants to find complementary or supportive data, or wants to find the right software to convert or analyze it. Instead of Farmville or Mafia Wars imagine useful tools that make these connections, negotiate content, and identify common context. As pointed out to me by Paul Walk this is very similar to what was envisioned in the 90s as the role of software agents. In this view the human research users are the poorly connected users on the outskirts of the web.

The point is that the hard part of creating linked data is making the links, not publishing the data. The semantic web has always suffered from the chicken and egg problem of a lack of user-friendly ways to generate RDF and few tools that could really use that RDF in exciting ways even if it did exist. I still can’t do a useful search on which restaurants in Bath will be open next Sunday. The reality is that the innards of this should be hidden from the user, the making of connections needs to be automated as far as possible, and as natural as possible when the user has to be involved. As easy as hitting that “like” button, or right clicking and adding a citation.

We have learnt a lot about the principles of when and how social networks work. If we can apply those lessons to the construction of open data management and discovery frameworks then we may stand some chance of actually making some of the original vision of the web work.

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“Friendfeeds for Science” pt II – Design ideas for a research focussed aggregator

Who likes me on friendfeed?
Image by cameronneylon via Flickr

This post, while only 48 hours old is somewhat outdated by these two Friendfeed discussions. This was written independently of those discussions so it seemed worth putting out in its original form rather than spending too much time rewriting.

I wrote recently about Sciencefeed, a Friendfeed like system aimed at scientists and was fairly critical. I also promised to write about what I thought a “Friendfeed for Researchers” should look like. To look at this we need to think about what Friendfeed, and other services including Twitter, Facebook, and Posterous are used for and what else they could do.

Friendfeed is an aggregator that enables, as I have written before, an “object-centric” means of interacting around those objects. As Alan Cann has pointed out this is not the only thing it does, also enabling the person-centric interactions that I see as more typical of Facebook and Twitter. Enabling both is important, as is the realization that all of these systems need to interoperate effectively with each other, something which is still evolving. But core to the development of something that works for researchers is that standard research objects and particularly papers, need to be first class objects. Author lists, one click to full text, one click to bookmark to my library.

Functionality 1: Treat research objects as first class citizens with special attention, start with journal papers and support for Citeulike/Zotero/Mendeley etc.

On top of this Friendfeed is a community, or rather several interlinked communities that have their own traditions, standards, and expectations, that are supported to a greater or lesser extent by the functionality of rooms, search, hiding, and administration found within Friendfeed. Any new service needs to understand and support these expectations.

Friendfeed also doesn’t so some things. It is not terribly effective as a bookmark tool, nor very good as tool for identifying and mining for objects or information that is more than a few days old although paradoxically it has served quite well as a means of archiving tweets and exposing them to search engines. The idea of a tool that surfaces objects to Google is an interesting one, and one we could take advantage of.  Granularity of sharing is also limited, what if I want slidesets to be public but tweets to be a private feed? Or to collect different feeds under different headings for different communities, public, domain-specific, and only for the interested specialist?

Finally Friendfeed doesn’t have a very sophisticated karma system.  While likes and comments will keep bringing specific objects (and by extension the people who have brought them in) into your attention stream there is none of the filtering power enabled by tools like StackOverflow. Whether or not such a thing is something we would want is an interesting question but it has the potential to enable much more sophisticated filtering and curation of content. StackOverflow itself has an interesting limitation as well; there is only one rank order of answers, I can’t choose to privelege the upmods of one specific curator rather than another. I certainly can’t choose to order my stream based on a persons upmods but not their downmods.

A user on Friendfeed plays three distinct roles, content author, content curator, and content consumer. Different people will emphasise different roles, from the pure broadcaster, to the pure reader who doesn’t ever interact. The real added value comes from the curation role and in particular enabling granular filtering based on your choice of curators. Curation comes in the form of choosing to push content to Friendfeed from outside servces, from “likes”, and from commenting. Commenting is both curation and authoring, providing context as well as providing new information or opinion. But supporting and validating this activity will be important. Whatever choice is made around “liking” or StackOverflow style up and down-modding needs to apply to comments as well as objects.

Functionality addition 2: Enable rating of comments and by extension, the people making them

If reputation gathering is to be useful in driving filtering functionality as I have suggested we will need good ways of separating content authoring from curation. One thing that really annoys me is seeing an interesting title and a friendly avatar on Friendfeed and clicking through to find something written by someone else. Not because I don’t want to read something written by someone else, but because my decision to click through was based on assumptions about who the author was.  We need to support a strong culture of citation and attribution in research. A Friendfeed for research will need to clearly mark the distinction between who has brought an object into the service, who has curated it, and who authored it. Both should be valued but the roles should be measured separately.

Functionality addition 3: Clearly designate authors and curators of objects brought into the stream. Possibly enable these activities to be rated separately?

If we recognize a role of author, outside that of the user’s curation activity we can also enable the rating of people and objects that don’t belong to users. This would allow researchers who are not users to build up reputation within the system. This has the potential to solve the “ghost town” phenomonen that plagues most science social networking sites. A new user could be able to claim the author role for objects that were originally brought  in by someone else. This would immediately connect them with other people who have commented on their work, and provide them with a reputation that can be further built upon through taking on curation activities.

This is a sensitive area, holding information on people without their knowledge, but it is something done already across indexing services, aggregation services, and chat rooms. The use of karma in this context would need to be very carefully thought out., and whether it would be made available either within or outside the system would be an important question to tackle.

Functionality addition 4: Collect reputation and comment information for authors who are not users to enable them to rapidly connect with relevant content if they choose to join.

Finally there is the question of interacting with this content and filtering it through the rating systems that have been created. The UI issues for this are formidable but there is a need to enable different views. A streaming view, and more static views of content a user has collected over long periods, as well as search. There is probably enough for another whole post in those issues.

Summary: Overall for me the key to building a service that takes inspiration from Friendfeed but delivers more functionality for researchers, while not alienating a wider potential user base is to build a tool that enables and supports curation rating and granular filtering of content. Authorship is key, as is quantitative measures of value and personal relevance that will enable users to build their own view of the content they are interested in, to collect it for themselves and to continue to curate it for themselves, either on their own or in collaboraton with others.

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Friendfeed for Research? First impressions of ScienceFeed

Image representing FriendFeed as depicted in C...
Image via CrunchBase

I have been saying for quite some time that I think Friendfeed offers a unique combination of functionality that seems to work well for scientists, researchers, and the people they want to (or should want to) have conversations with. For me the core of this functionality lies in two places: first that the system explicitly supports conversations that centre around objects. This is different to Twitter which supports conversations but doesn’t centre them around the object – it is actually not trivial to find all the tweets about a given paper for instance. Facebook now has similar functionality but it is much more often used to have pure conversation. Facebook is a tool mainly used for person to person interactions, it is user- or person-centric. Friendfeed, at least as it is used in my space is object-centric, and this is the key aspect in which “social networks for science” need to differ from the consumer offerings in my opinion. This idea can trace a fairly direct lineage via Deepak Singh to the Jeff Jonas/Jon Udell concatenation of soundbites:

“Data finds data…then people find people”

The second key aspect about Friendfeed is that it gives the user a great deal of control over what they present to represent themselves. If we accept the idea that researchers want to interact with other researchers around research objects then it follows that the objects that you choose to represent yourself is crucial to creating your online persona. I choose not to push Twitter into Friendfeed mainly because my tweets are directed at a somewhat different audience. I do choose to bring in video, slides, blog posts, papers, and other aspects of my work life. Others might choose to include Flickr but not YouTube. Flexibility is key because you are building an online presence. Most of the frustration I see with online social tools and their use by researchers centres around a lack of control in which content goes where and when.

So as an advocate of Friendfeed as a template for tools for scientists it is very interesting to see how that template might be applied to tools built with researchers in mind. ScienceFeed launched yesterday by Ijad Madisch, the person behind ResearchGate. The first thing to say is that this is an out and out clone of Friendfeed, from the position of the buttons to the overall layout. It seems not to be built on the Tornado server that was open sourced by the Friendfeed team so questions may hang over scalability and architecture but that remains to be tested. The main UI difference with Friendfeed is that the influence of another 18 months of development of social infrastructure is evident in the use of OAuth to rapidly leverage existing networks and information on Friendfeed, Twitter, and Facebook. Although it still requires some profile setup, this is good to see. It falls short of the kind of true federation which we might hope to see in the future but then so does everything else.

In terms of specific functionality for scientists the main additions is a specialised tool for adding content via a search of literature databases. This seems to be adapted from the ResearchGate tool for populating a profile’s publication list. A welcome addition and certainly real tools for researchers must treat publications as first class objects. But not groundbreaking.

The real limitation of ScienceFeed is that it seems to miss the point of what Friendfeed is about. There is currently no mechanism for bringing in and aggregating diverse streams of content automatically. It is nice to be able to manually share items in my citeulike library but this needs to happen automatically. My blog posts need to come in as do my slideshows on slideshare, my preprints on Nature Precedings or Arxiv. Most of this information is accessible via RSS feeds so import via RSS/Atom (and in the future real time protocols like XMPP) is an absolute requirement. Without this functionality, ScienceFeed is just a souped up microblogging service. And as was pointed out yesterday in one friendfeed thread we have a twitter-like service for scientists. It’s called Twitter. With the functionality of automatic feed aggregation Friendfeed can become a presentation of yourself as a researcher on the web. An automated publication list that is always up to date and always contains your latest (public) thoughts, ideas, and content. In short your web-native business card and CV all rolled into one.

Finally there is the problem of the name. I was very careful at the top of this post to be inclusive in the scope of people who I think can benefit from Friendfeed. One of the great strengths of Friendfeed is that it has promoted conversations across boundaries that are traditionally very hard to bridge. The ongoing collision between the library and scientific communities on Friendfeed may rank one day as its most important achievement, at least in the research space. I wonder whether the conversations that have sparked there would have happened at all without the open scope that allowed communities to form without prejudice as to where they came from and then to find each other and mingle. There is nothing in ScienceFeed that precludes anyone from joining as far as I can see, but the name is potentially exclusionary, and I think unfortunate.

Overall I think ScienceFeed is a good discussion point, a foil to critical thinking, and potentially a valuable fall back position if Friendfeed does go under. It is a place where the wider research community could have a stronger voice about development direction and an opportunity to argue more effectively for business models that can provide confidence in a long term future. I think it currently falls far short of being a useful tool but there is the potential to use it as a spur to build something better. That might be ScienceFeed v2 or it might be an entirely different service. In a follow-up post I will make some suggestions about what such a service might look like but for now I’d be interested in what other people think.

Other Friendfeed threads are here and here and Techcrunch has also written up the launch.

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The trouble with business models (Facebook buys Friendfeed)

…is that someone needs to make money out of them. It was inevitable at some point that Friendfeed would take a route that lead it towards mass adoption and away from the needs of the (rather small) community of researchers that have found a niche that works well for them. I had thought it more likely that Friendfeed would gradually move away from the aspects that researchers found attractive rather than being absorbed wholesale by a bigger player but then I don’t know much about how Silicon Valley really works. It appears that Friendfeed will continue in its current form as the two companies work out how they might integrate the functionality into Facebook but in the long term it seems unlikely that current service will survive. In a sense the sudden break may be a good thing because it forces some of the issues about providing this kind of research infrastructure out into the open in a way a gradual shift probably wouldn’t.

What is about Friendfeed that makes it particularly attractive to researchers? I think there are a couple of things, based more on hunches than hard data but in comparing with services like Twitter and Facebook there are a couple of things that standout.

  1. Conversations are about objects. At the core of the way Friendfeed works are digital objects, images, blog posts, quotes, thoughts, being pushed into a shared space. Most other services focus on the people and the connections between them. Friendfeed (at least the way I use it) is about the objects and the conversations around them.
  2. Conversation is threaded and aggregated. This is where Twitter loses out. It is almost impossible to track a specific conversation via Twitter unless you do so in real time. The threaded nature of FF makes it possible to track conversations days or months after they happen (as long as you can actually get into them)
  3. Excellent “person discovery” mechanisms. The core functionality of Friendfeed means that you discover people who “like” and comment on things that either you, or your friends like and comment on. Friendfeed remains one of the most successful services I know of at exploiting this “friend of a friend” effect in a useful way.
  4. The community. There is a specific community, with a strong information technology, information management, and bioinformatics/structural biology emphasis, that grew up and aggregated on Friendfeed. That community has immense value and it would be sad to lose it in any transition.

So what can be done? One option is to set back and wait to be absorbed into Facebook. This seems unlikely to be either feasible or popular. Many people in the FF research community don’t want this for reasons ranging from concerns about privacy, through the fundamentals of how Facebook works, to just not wanting to mix work and leisure contacts. All reasonable and all things I agree with.

We could build our own. Technically feasible but probably not financially. Lets assume a core group of say 1000 people (probably overoptimistic) each prepared to pay maybe $25 a year subscription as well as do some maintenance or coding work. That’s still only $25k, not enough to pay a single person to keep a service running let alone actually build something from scratch. Might the FF team make some of the codebase Open Source? Obviously not what they’re taking to Facebook but maybe an earlier version? Would help but there would still need to be either a higher subscription or many more subscribers to keep it running I suspect. Chalk one up for the importance of open source services though.

Reaggregating around other services and distributing the functionality would be feasible perhaps. A combination of Google Reader, Twitter, with services like Tumblr, Posterous, and StoryTlr perhaps? The community would be likely to diffuse but such a distributed approach could be more stable and less susceptible to exactly this kind of buy out. Nonetheless these are all commercial services that can easily dissappear. Google Wave has been suggested as a solution but I think has fundamental differences in design that make it at best a partial replacement. And it would still require a lot of work.

There is a huge opportunity for existing players in the Research web space to make a play here. NPG, Research Gate, and Seed, as well as other publishers or research funders and infrastructure providers (you know who you are) could fill this gap if they had the resource to build something. Friendfeed is far from perfect, the barrier to entry is quite high for most people, the different effective usage patterns are unclear for new users. Building something that really works for researchers is a big opportunity but it would still need a business model.

What is clear is that there is a signficant community of researchers now looking for somewhere to go. People with a real critical eye for the best services and functionality and people who may even be prepared to pay something towards it. And who will actively contribute to help guide design decisions and make it work. Build it right and we may just come.

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Some slides for granting permissions (or not) in presentations

A couple of weeks ago there was a significant fracas over Daniel MacArthur‘s tweeting from a Cold Spring Harbour Laboratory meeting.  This was followed in pretty quick succession by an article in Nature discussing the problems that could be caused when the details of presentations no longer stop at the walls of the conference room and all of these led to a discussion (see also friendfeed discussions) about how to make it clear whether you are happy or not with your presentation being photographed, videoed, or live blogged. A couple of suggestions were made for logos or icons that might be used.

I thought it might be helpful rather than a single logo to have a panel that allows the presenter to permit some activities but not others and put together a couple of mockups.

Permission to do whatever with presentationPermission to do less with presentation

I’ve also uploaded a PowerPoint file with the two of these as slides to Slideshare which should enable you to download, modify, and extract the images as you wish. In both cases they are listed as having CC-BY licences but feel free to use them without any attribution to me.

In some of the Friendfeed conversations there are some good comments about how best to represent and suggestions on possible improvements. In particular Anders Norgaard suggests a slightly more friendly “please don’t” rather than my “do not”. Entirely up to you, but I just wanted to get these out. At the moment these are really just to prompt discussion but if you find them useful then please re-post modified versions for others to use.

[Ed. The social media icons are from Chris Ross and are by default under a GPL license. I have a request in to make them available to the Public Domain or as CC-BY at least for re-use. And yes I should have picked this up before.]

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“Real Time”: The next big thing or a pointer to a much more interesting problem?

There has been a lot written and said recently about the “real time” web most recently in an interview of Paul Buchheit on ReadWriteWeb. The premise is that if items and conversations are carried on in “real time” then they are more efficient and more engaging. The counter argument has been that they become more trivial. That by dropping the barrier to involvement to near zero, the internal editorial process that forces each user to think a little about what they are saying, is lost generating a stream of drivel. I have to admit upfront that I really don’t get the excitement. It isn’t clear to me that the difference between a five or ten second refresh rate versus a 30 second one is significant.

In one sense I am all for getting a more complete record onto the web, at least if there is some probability of it being archived. After all this is what we are trying to do with the laboratory recording effort; creat as complete a record on the web as possible. But at some point there is always going to be an editorial process. In a blog it takes some effort to write a post and publish it, creating a barrier which imposes some editorial filter. Even on Twitter the 140 character limit forces people to be succinct and often means a pithy statement gets refined before hitting return. In an IM or chat window you will think before hitting return (hopefully!). Would true “real time” mean watching as someone typed or would it have to be a full brain dump as it happened? I’m not sure I want either of these, if I want real time conversation I will pick up the phone.

But while everyone is focussed on “real time” I think it is starting to reveal a more interesting problem. One I’ve been thinking about for quite a while but have been unable to get a grip on. All of these services have different intrinsic timeframes. One of the things I dislike about the new FriendFeed interface is the “real time” nature of it. What I liked previously was that it had a slower intrinsic time than, say, Twitter or instant messenging, but a faster intrinsic timescale than a blog or email. On Twitter/IM conversations are fast, seconds to minutes, occassionally hours. On FriendFeed they tend to run from minutes to hours, with some continuing on for days, all threaded and all kept together. Conversations in blog comments run over hours, to days, email over days, newspapers over weeks, academic literature over months and years.

Different people are comfortable with interacting with streams running at these different rates. Twitter is too much for some, as is FriendFeed, or online content at all. Many don’t have time to check blog comments, but perhaps are happy to read the posts once a day. But these people probably appreciate that the higher rate data is there. Maybe they come across an interesting blog post referring to a comment and want to check the comment, maybe the comment refers to a conversation on Twitter and they can search to find that. Maybe they find a newspaper article that leads to a wiki page and on to a pithy quote from an IM service. This type of digging is enabled by good linking practice. And it is enabled by a type of social filtering where the user views the stream at a speed which is compatible with their own needs.

The tools and social structures are well developed now for this kind of social filtering where a user outsources that function to other people, whether they are on FriendFeed, or are bloggers or traditional dead-tree journalist. What I am less sure about is the tooling for controlling the rate of the stream that I am taking in. Deepak wrote an interesting post recently on social network filtering, with the premise that you needed to build a network that you trusted to bring important material to your attention. My response to this is that there is a fundamental problem that, at the moment, you can’t independently control both the spread of the net you set, and the speed at which information comes in. If you want to cover a lot of areas you need to follow a lot of people and this means the stream is faster.

Fundamentally, as the conversation has got faster and faster, no-one seems to be developing tools that enable us to slow it down. Filtering tools such as those built into Twitter clients help. One of the things I do like about the new Friendfeed interface is the search facility that allows you to set filters that display only those items with a certain number of “likes” or comments help. But what I haven’t seen are tools that are really focussed on controlling the rate of a stream, that work to help you optimize your network to provide both spread and rate. And I haven’t seen much thought go into tools or social practices that enable you to bump an item from one stream to a slower stream to come back to later. Delicious is the obvious tool here; bookmarking objects for later attention, but how many people actually go back to their bookmarks on a regular basis and check over them?

Dave Allen probably best described the concept of a “Tickler File“, a file where you place items into a date marked slot based on when you think you need to be reminded about them.  The way some people regularly review their recent bookmarks and then blog the most interesting ones is an example of a process that achives the same thing. I think this is probably a good model to think about. A tool, or set of practices, that park items for a specified, and item or class specific, period of time and then pulls them back up and puts them in front of you. Or perhaps does it in a context dependent fashion, or both, picking the right moment in a specific time period to have it pop up. Ideally it will also put them, or allow you to put them, back in front of your network for further consideration as well. We still want just the one inbox for everything. It is a question of having control over the intrinsic timeframes of the different streams coming into it, including streams that we set up for ourselves.

As I said, I really haven’t got a good grip on this, but my main point is that I think Real Time is just a single instance of giving users access to one specific intrinsic timeframe. The much more interesting problem, and what I think will be one of the next big things is the general issue of giving users temporal control within a service, particularly for enterprise applications.

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Use Cases for Provenance – eScience Institute – 20 April

On Monday I am speaking as part of a meeting on Use Cases for Provenance (Programme), which has a lot of interesting talks scheduled. I appear to be last. I am not sure whether that means I am the comedy closer or the pre-dinner entertainment. This may, however, be as a result of the title I chose:

In your worst nightmares: How experimental scientists are doing provenance for themselves

On the whole experimental scientists, particularly those working in traditional, small research groups, have little knowledge of, or interest in, the issues surrounding provenance and data curation. There is however an emerging and evolving community of practice developing the use of the tools and social conventions related to the broad set of web based resources that can be characterised as “Web 2.0”. This approach emphasises social, rather than technical, means of enforcing citation and attribution practice, as well as maintaining provenance. I will give examples of how this approach has been applied, and discuss the emerging social conventions of this community from the perspective of an insider.

The meeting will be webcast (link should be available from here) and my slides will with any luck be up at least a few minutes before my talk in the usual place.

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Euan Adie asks for help characterising PLoS comments

Euan Adie has asked for some help to do further analysis on the comments made on PLoS ONE articles. He is doing this via crowd sourcing through a specially written app at appspot to get people to characterize all the comments in PLoS ONE. Euan is very good at putting these kind of things together and again this shows the power of Friendfeed as a way of getting the message out. Dividing the job up into bite sized chunks so people can help even with a little bit of time, providing the right tools, and getting them in the hands of people who care enough to dedicate a little time. If anything counts as Science2.0 then this must be pretty close.