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Salmon, an aggregation protocol, is championed by Google’s John Panzer, and described as an “an open, simple, standards-based solution” for “unifying the conversations”.

‘Conversations’ is deliberately plural, I think, to evoke the many conversations, invisible to one another: “The comments, ratings, and annotations increasingly happen at the aggregator and are invisible to the original source.”

Using Salmon, an aggregator pushes comments back to a “Salmon endpoint” (via POST). These can be published (or moderated) upstream at the original source. See also the summary of the Salmon protocol.

Comments swimming upstream…

In a post at ZDNet, Dion Hinchcliffe delineates 7 problems of today’s social web:

Fragmentation of conversation.

Disconnects between older and newer generations of social media

Lack of control of identity, contacts, and data.

A better social Web on mobile devices.

Poor integration between social media and location services.

Difficulty of coherently engaging in social activity across many channels.

Coping with and getting value from the expanding information volume of social media.

from “The social Web in 2010: The emerging standards and technologies to watch” encountered via Ed H. Chi’s post at the PARC Augmented Social Cognition blog.

The trends? Openness, portability, aggregation of distributed content. Hopefully we’ll see more on all these fronts in 2010 and beyond. Hinchcliffe also suggests that we want “Better social and location capabilities added to the core of mobile devices.”

See the full post at ZDNet for more discussion and references to a number of standards, formats, and related developments. In the next post, I’ll highlight Salmon, a protocol for distributed commenting, which I’d neither encountered nor heard of.

Here’s a taxonomy of tweets from an experiment at SemanticHacker Blog:

• User’s current status
• Private conversations
• Links to web content
  • links to blog and news articles
  • links to images and videos
  • other links
• Politics, sports, current events
• Product recommendations/complaints
• Advertising  “posted from a company’s twitter account”
• Spam
• Other messages “that don’t quite fit under any of the above categories. Fan messages to celebrities, shoutouts to other users, web-based polls and quizzes, and so on.”

via Hak-Lae Kim on twitter

Based in Silicon Valley, Ribbit is an internet telephony startup and a subsidiary of British Telcom. Ribbit has an “open platform for voice innovation”, with API access for developers (see also getting started) and several end-user products.

Ribbit Mobile is similar to Google Voice: it’s a next-generation phone system currently aimed at the US and UK markets. You use your own (presumably mobile) number. What really got my attention, though, was a new social feature they call “Caller ID 2.0″:

Ribbit wants to leverage your social networks for Caller ID

When a call comes in, Ribbit Mobile will reach into the social web and bring you the recent LinkedIn updates, Facebook updates, Tweets, and Flickr photos of the person calling you. Ribbit Mobile lets you know not just who is calling but what the caller has been up to on the web.

I was already excited about Ribbit when I first encountered them.

I guess it’s time to try out their Google Wave gadgets! And if Google Voice or VoIP with your mobile number has any appeal, I’d advise you to request an invite for their beta. Note that they’ve already got plans to charge for their services.

Rights to data may depend, says Bruce Schneier, on what type of data it is and who provided it. He provides a useful enumeration:

1. Service data. Service data is the data you need to give to a social networking site in order to use it. It might include your legal name, your age, and your credit card number.

2. Disclosed data. This is what you post on your own pages: blog entries, photographs, messages, comments, and so on.

3. Entrusted data. This is what you post on other people’s pages. It’s basically the same stuff as disclosed data, but the difference is that you don’t have control over the data — someone else does.

4. Incidental data. Incidental data is data the other people post about you. Again, it’s basically same same stuff as disclosed data, but the difference is that 1) you don’t have control over it, and 2) you didn’t create it in the first place.

5. Behavioral data. This is data that the site collects about your habits by recording what you do and who you do it with.

See Schenier’s post for discussion. Via a pointer on Rob Styles’ blog, in turn via Rob’s tweet.

Have you come across other taxonomies for social networking data?

Here’s a simple but far less expressive one way to characterize data on social networks. Is it “about you” or “from you”? Either the first, the second, neither, or both. “Aboutness”, however, is ontologically challenging. Any use for this?

Collaboration/shared control isn’t considered in this taxonomy. For instance, “entrusted data” doesn’t capture the notion of “shared data” in a collaborative system such as wave, a wiki, or perhaps even email.

For behavioral data in libraries, see also “intentional data”, as used by Lorcan Dempsey, back to 2005 (and many times since) [for instance, in discussion with "emergent knowledge"]. I prefer “behavioral data” since much data about intention is by no means deliberate/intentional!