Here are the 3 key questions and the most closely related work that I’ve identified in the first 9 months of my Ph.D.

Read on, if you want to know more. Then let me know what you think! Suggestions will be especially helpful since I’m writing my first year Ph.D. report, which will set the direction for my second year at DERI.

Enabling a Social Semantic Web for Argumentation

Argumentative discussions occur informally throughout the Web, however there is currently no way of bringing together all of the discussions on a given topic along with an indication of who is agreeing and who is disagreeing. Thus substantial human analysis is required to integrate opinions and expertise to, for instance, determine the best policies and procedures to mitigate global warming, or the recommended treatment for a given disease. New techniques for gathering and organising the Social Web using ontologies such as FOAF and SIOC show promise for creating a Social Semantic Web for argumentation.

I am currently investigating three main research questions to establish the Social Semantic Web for argumentation:

1. How can we best define argumentation for the Social Semantic Web, to isolate the essential problems? We wish to enable reasoning with inconsistent knowledge, to integrate disparate knowledge, and identify consensus and disputes.  Similar questions and techniques come up in related but distinct areas, such as sentiment analysis, dialogue mapping, dispute resolution, question-answering and e-government participation.
2. What sort of modular framework for argumentation can support distributed, emergent argumentation — a World Wide Argumentation Web? Some Web 2.0 tools, such as Debatepedia, LivingVote, and Debategraph, provide integrated environments for explicit argumentation. But our goal is for individuals to be able to use their own preferred tools — in a social environment — while understanding what else is being discussed.
3. How can we manage the tension between informality and ease of expression on the one hand and formal semantics and retrievability/reusability on the other hand? Minimal integration of informal arguments requires two pieces of information: a statement of the issue or proposition, and an indication of polarity (agreement or disagreement). How can we gather this information without adding cognitive overhead for users?

Related Work

Ennals et al. ask: ‘What is disputed on the Web? (Ennals 2010b). They use annotation and NLP techniques to develop a prototype system for highlighting disputed claims in Web documents (Ennals 2010a). Cabanac et al. find that two algorithms for identifying the level of controversy about an issue were up to 84% accurate (compared to human perception), on a corpus of 13 arguments. These are useful prototypes of what could be done; Ennals prototype is indeed a Web-scale system, but disputed claims are not arguments.

Rahwan et al. (2007) present a pilot Semantic Web-based system, ArgDF, in which users can create arguments, and query to find networks of arguments. ArgDF is backed with the AIF-RDF ontology, and uses Semantic Web standards.  Rahwan (2008) surveys current Web2.0 tools, pointing out that integration between these tools is lacking, and that only very shallow argument structures are supported; ArgDF and AIF-RDF are explained as an improvement. What is lacking is uptake in end-user orientated (e.g. Web 2.0) tools.

The Web2.0 aspect of the problem is explored in several papers, including Buckingham Shum (2008), which presents Cohere, a Web2.0-style argumentation system supporting existing (non-Semantic Web) argumentation standards, and Groza et al. (2009) which proposes a abstract framework for modeling argumentation. These are either minimally implemented frameworks or stand-alone systems which do not yet support the distributed, emergent argumentation envisioned, as further elucidated by Buckingham Shum (2010).

References with links to preprints

1. S. Buckingham Shum, “Cohere: Towards Web 2.0 Argumentation,” Computational Models of Argument – Proceedings of COMMA 2008, IOS Press, 2008.
2. S. Buckingham Shum, AIF Use Case: Iraq Debate, Glenshee, Scotland, UK: 2010. http://projects.kmi.open.ac.uk/hyperdiscourse/docs/AIF-UseCase-v2.pdf
3. G. Cabanac, M. Chevalier, C. Chrisment, and C. Julien, “Social validation of collective annotations: Definition and experiment,” Journal of the American Society for Information Science and Technology, vol. 61, 2010, pp. 271-287.
4. R. Ennals, B. Trushkowsky, and J.M. Agosta, “Highlighting Disputed Claims on the Web,” WICOW 2010, Raleigh, North Carolina: 2010.
5. R. Ennals, D. Byler, J.M. Agosta, and Barboara Rosario, “What is Disputed on the Web?,” WWW 2010, Raleigh, North Carolina: 2010.
6. T. Groza, S. Handschuh, J.G. Breslin, and S. Decker, “An Abstract Framework for Modeling Argumentation in Virtual Communities,” International Journal of Virtual Communities and Social Networking, vol. 1, Sep. 2009, pp. 35-47. 
7. I. Rahwan, “Mass argumentation and the semantic web,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 6, Feb. 2008, pp. 29-37.
8. I. Rahwan, F. Zablith, and C. Reed, “Laying the foundations for a World Wide Argument Web,” Artificial Intelligence, vol. 171, Jul. 2007, pp. 897-921.

‘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…

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.

• 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

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.

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!